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Statistics 2014–2015 Catalog CLA DEC 2013 Cengage Learning Asia Pte Ltd 151 Lorong Chuan #02-08 New Tech Park (Lobby H) Singapore 556741 Tel (65) 6410 1200 Fax (65) 6410 1208 e-mail [email protected] www.cengageasia.com www.cengageasia.com Cengage Learning Asia introducing the new . . . Cengage Technology Editions are being launched to support educators and learners in making a smooth transition from print to digital learning and instruction. These hybrid editions combine full ebooks or digital solutions packages with print editions for maximum flexibility and benefits, including: enhanced student-teacher interactions engaged students, improved learning experiences and better outcomes a growing and interconnected pool of best-selling titles from Cengage Learning Cengage Technology Editions offer the best in print and digital content to create the kinds of blended learning experiences that are fast gaining popularity among students. These editions provide hybrid alternatives to best-selling titles from Cengage Learning in disciplines such as Business and Economics, Science and Mathematics, Social Sciences and Humanities, and more. Engage with Us! Email [email protected] • www.cengageasia.com Contents Mathematics Introductory Statistics�� 2 Statistics Intermediate/Advanced Statistics�� 22 Introductory Statistics��46 Software / Handbooks / Courseware / Casebooks��96 Combined Author/Title Index��99 Prices Order form/pricelist is available upon request through Cengage Learning Representative. Special prices are available for class adoption. Please contact your Cengage Learning Representative, listed in the inside backcover of this catalog for information on class adoption. Prices are subject to change without prior notice. Mathematics Introductory Statistics ELEMENTARY STATISTICS Looking at the Big Picture Nancy Pfenning, University of Pittsburgh Using a successfully class-tested approach that gives coherence to a broad range of introductory topics, this innovative text provides students with a real-world, big picture view of statistics as well as problemsolving strategies that can be applied to the statistical questions, real data, and examples that they will encounter. Author Nancy Pfenning organizes content around four basic processes of statistics: producing data, displaying and summarizing data, understanding probability, and using probability to perform statistical inference. Within this framework, the book progresses systematically through five basic problem situations involving values of variables (quantitative, categorical, or a blend). As a result, students learn to identify which situation applies and how to choose the correct display, summary, or inference tool or technique. As students gain proficiency in specific statistical techniques, the author also points out connections among topics and techniques. More than 1,000 real-life examples and categorized exercises support the approach, engaging students in practicing and developing a variety of skills. FEATURES • Big picture problem-solving approach--An innovative approach organizes content around four processes of statistics and the basic situations that apply to any statistical problem. Students learn specific techniques while gaining a broad perspective on statistics along with strategies for choosing the appropriate technique for any research situation. • Flexible content and technology integration--The presentation of each topic is self-contained enough to allow for more advanced topics such as ANOVA or chi-square to be skipped if desired with no loss in course coherence. A “Using Software” section near the end of each chapter makes it easy for instructors to emphasize the use of software in the course, or choose not to do so. • Examples using real data--Hundreds of well-chosen examples, based on current data from a wide variety of statistical applications, clarify the presentation of ideas. Each example follows a consistent format: Background, Question, Response, and Practice Exercise. Data sets feature values that allow easy entering by hand into a computer or calculator; they are also available at the Book Companion Website. • Plentiful categorized exercises, computational and conceptual--A set of problems after each major section assures that students practice basic concepts as they are introduced. A more comprehensive set at the end of each chapter (“Warming Up,” “Exploring the Big Picture,” “Using Software,” “Discovering Research,” and “Reporting on Research”) encourages students to integrate individual topics. Like the examples, exercises cover a variety of topics and use real data. • Emphasis on connections among topics--Strategically placed marginal notes show how each new topic fits into the larger framework of statistics, relating it to material covered earlier as well as to content that follows. These connections can be digested by students at their own pace, without disrupting the flow of main ideas. CONTENTS 1. Introduction: Variables and Processes in Statistics. Types of Variables: Categorical or Quantitative. Students Talk Stats: Identifying Types of Variables. Handling. Data for Two Types of Variables. Roles of Variables: Explanatory or Response. Statistics as a Four-Stage Process. PART I: DATA PRODUCTION. 2. Sampling: Which Individuals Are Studied. Sources of Bias in Sampling: When Selected Individuals Are Not Representative. Probability Sampling Plans: Relying on Randomness. Role of Sample Size: Bigger Is Better if the Sample Is Representative. From Sample to Population: To What Extent Can We Generalize? Students Talk www.cengageasia.com 2 Stats: Seeking a Representative Sample. 3. Design: How Individuals Are Studied. Various Designs for Studying Variables. Sample Surveys: When Individuals Report Their Own Values. Observational Studies: When Nature Takes Its Course. Experiments: When Researchers Take Control. Students Talk Stats: Does TV Cause ADHD? Considering Study Design. PART II: DISPLAYING AND SUMMARIZING DATA. 4. Displaying and Summarizing Data for a Single Variable. Single Categorical Variable. Students Talk Stats: Biased Sample, Biased Assessment. Single Quantitative Variables and the Shape of a Distribution. Center and Spread: What’s Typical for Quantitative Values, and How They Vary. Normal Distributions: The Shape of Things to Come. 5. Displaying and Summarizing Relationships. Relationship Between One Categorical and One Quantitative Variable. Students Talk Stats: Displaying and Summarizing Paired Data. Relationship Between Two Categorical Variables. Relationships Between Two Quantitative Variables. Students Talk Stats: How Outliers and Influential Observations Affect a Relationship. Students Talk Stats: Confounding in a Relationship Between Two Quantitative Variables. PART III: PROBABILITY. 6. Finding Probabilities. The Meaning of “Probability” and Basic Rules. More General Probability Rules and Conditional Probability. Students Talk Stats: Probability as a Weighted Average of Conditional Probabilities. 7. Random Variables. Discrete Random Variables. Binomial Random Variables. Students Talk Stats: Calculating and Interpreting the Mean and Standard Deviation of Count or Proportion. Continuous Random Variables and the Normal Distribution. Students Talk Stats: Means, Standard Deviations, and Below-Average Heights. 8. Sampling Distributions. The Behavior of Sample Proportion in Repeated Random Samples. The Behavior of Sample Mean in Repeated Random Samples. Students Talk Stats: When Normal Approximations Are Appropriate. PART IV: STATISTICAL INFERENCE. 9. Inference for a Single Categorical Variable. Point Estimate and Confidence Interval: A Best Guess and a Range of Plausible Values for Population Proportion. Students Talk Stats: Interpreting a Confidence Interval. Test: Is a Proposed Population Proportion Plausible? Students Talk Stats: Interpreting a P-value. Students Talk Stats: What Type of Error Was Made? Students Talk Stats: The Correct Interpretation of a Small P-value. Students Talk Stats: The Correct Interpretation When a P-value Is Not Small. 10. Inference for a Single Quantitative Variable. Inference for a Mean when Population Standard Deviation Is Known or Sample Size Is Large. Students Talk Stats: Confidence Interval for a Mean. Students Talk Stats: Interpreting a Confidence Interval for the Mean Correctly. Inference for a Mean When the Population Standard Deviation Is Unknown and the Sample Size Is Small. Students Talk Stats: Practical Application of a t Test. A Closer Look at Inference for Means. 11. Inference for Relationships Between Categorical and Quantitative Variables. Inference for a Paired Design with t. Inference for a Two-Sample Design with t. Students Talk Stats: Ordinary Vs. Pooled TwoSample t. Inference for a Several-sample Design with F: Analysis of Variance. Students Talk Stats: Reviewing Relationships between Categorical and Quantitative Variables. 12. Inference for Relationships Between Two Categorical Variables. Comparing Proportions with a z Test. Comparing Counts with a Chi-Square Test. 13. Inference for Relationships Between Two Quantitative Variables. Inference for Regression: Focus on the Slope of the Regression Line. Students Talk Stats: No Evidence of a Relationship. Interval Estimates for an Individual or Mean Response. 14. How Statistics Problems Fit into the Big Picture. The Big Picture in Problem-Solving. Students Talk Stats: Choosing the Appropriate Statistical Tools. 15. Non-Parametric Methods (Online). The Sign Test as an Alternative to the Paired t Test. The Rank-Sum Test as an Alternative to the Two-Sample t Test. Summary of Non-Parametrics. 16. Two-Way ANOVA (Online). 17. Multiple Regression (Online). PART V: SOLUTIONS TO SELECTED EXERCISES. © 2011, 784pp, Hardback, 9780495016526 www.cengageasia.com 3 ELEMENTARY STATISTICS Looking at the Big Picture, International Edition Nancy Pfenning, University of Pittsburgh Using a successfully class-tested approach that gives coherence to a broad range of introductory topics, this innovative text provides students with a real-world, big picture view of statistics as well as problemsolving strategies that can be applied to the statistical questions, real data, and examples that they will encounter. Author Nancy Pfenning organizes content around four basic processes of statistics: producing data, displaying and summarizing data, understanding probability, and using probability to perform statistical inference. Within this framework, the book progresses systematically through five basic problem situations involving values of variables (quantitative, categorical, or a blend). As a result, students learn to identify which situation applies and how to choose the correct display, summary, or inference tool or technique. As students gain proficiency in specific statistical techniques, the author also points out connections among topics and techniques. More than 1,000 real-life examples and categorized exercises support the approach, engaging students in practicing and developing a variety of skills. FEATURES • Big picture problem-solving approach—An innovative approach organizes content around four processes of statistics and the basic situations that apply to any statistical problem. Students learn specific techniques while gaining a broad perspective on statistics along with strategies for choosing the appropriate technique for any research situation. • Flexible content and technology integration—The presentation of each topic is self-contained enough to allow for more advanced topics such as ANOVA or chi-square to be skipped if desired with no loss in course coherence. A “Using Software” section near the end of each chapter makes it easy for instructors to emphasize the use of software in the course, or choose not to do so. • Examples using real data—Hundreds of well-chosen examples, based on current data from a wide variety of statistical applications, clarify the presentation of ideas. Each example follows a consistent format: Background, Question, Response, and Practice Exercise. Data sets feature values that allow easy entering by hand into a computer or calculator; they are also available at the Book Companion Website. • Plentiful categorized exercises, computational and conceptual—A set of problems after each major section assures that students practice basic concepts as they are introduced. A more comprehensive set at the end of each chapter (“Warming Up,” “Exploring the Big Picture,” “Using Software,” “Discovering Research,” and “Reporting on Research”) encourages students to integrate individual topics. Like the examples, exercises cover a variety of topics and use real data. • Emphasis on connections among topics—Strategically placed marginal notes show how each new topic fits into the larger framework of statistics, relating it to material covered earlier as well as to content that follows. These connections can be digested by students at their own pace, without disrupting the flow of main ideas. CONTENTS 1. Introduction: Variables and Processes in Statistics. Types of Variables: Categorical or Quantitative. Students Talk Stats: Identifying Types of Variables. Handling. Data for Two Types of Variables. Roles of Variables: Explanatory or Response. Statistics as a Four-Stage Process. PART I: DATA PRODUCTION. 2. Sampling: Which Individuals Are Studied. Sources of Bias in Sampling: When Selected Individuals Are Not Representative. Probability Sampling Plans: Relying on Randomness. Role of Sample Size: Bigger Is Better if the Sample Is Representative. From Sample to Population: To What Extent Can We Generalize? Students Talk Stats: Seeking a Representative Sample. 3. Design: How Individuals Are Studied. Various Designs for Studying Variables. Sample Surveys: When Individuals Report Their Own Values. Observational Studies: When Nature Takes Its Course. Experiments: When Researchers Take www.cengageasia.com 4 Control. Students Talk Stats: Does TV Cause ADHD? Considering Study Design. PART II: DISPLAYING AND SUMMARIZING DATA. 4. Displaying and Summarizing Data for a Single Variable. Single Categorical Variable. Students Talk Stats: Biased Sample, Biased Assessment. Single Quantitative Variables and the Shape of a Distribution. Center and Spread: What’s Typical for Quantitative Values, and How They Vary. Normal Distributions: The Shape of Things to Come. 5. Displaying and Summarizing Relationships. Relationship Between One Categorical and One Quantitative Variable. Students Talk Stats: Displaying and Summarizing Paired Data. Relationship Between Two Categorical Variables. Relationships Between Two Quantitative Variables. Students Talk Stats: How Outliers and Influential Observations Affect a Relationship. Students Talk Stats: Confounding in a Relationship Between Two Quantitative Variables. PART III: PROBABILITY. 6. Finding Probabilities. The Meaning of “Probability” and Basic Rules. More General Probability Rules and Conditional Probability. Students Talk Stats: Probability as a Weighted Average of Conditional Probabilities. 7. Random Variables. Discrete Random Variables. Binomial Random Variables. Students Talk Stats: Calculating and Interpreting the Mean and Standard Deviation of Count or Proportion. Continuous Random Variables and the Normal Distribution. Students Talk Stats: Means, Standard Deviations, and Below-Average Heights. 8. Sampling Distributions. The Behavior of Sample Proportion in Repeated Random Samples. The Behavior of Sample Mean in Repeated Random Samples. Students Talk Stats: When Normal Approximations Are Appropriate. PART IV: STATISTICAL INFERENCE. 9. Inference for a Single Categorical Variable. Point Estimate and Confidence Interval: A Best Guess and a Range of Plausible Values for Population Proportion. Students Talk Stats: Interpreting a Confidence Interval. Test: Is a Proposed Population Proportion Plausible? Students Talk Stats: Interpreting a P-value. Students Talk Stats: What Type of Error Was Made? Students Talk Stats: The Correct Interpretation of a Small P-value. Students Talk Stats: The Correct Interpretation When a P-value Is Not Small. 10. Inference for a Single Quantitative Variable. Inference for a Mean when Population Standard Deviation Is Known or Sample Size Is Large. Students Talk Stats: Confidence Interval for a Mean. Students Talk Stats: Interpreting a Confidence Interval for the Mean Correctly. Inference for a Mean When the Population Standard Deviation Is Unknown and the Sample Size Is Small. Students Talk Stats: Practical Application of a t Test. A Closer Look at Inference for Means. 11. Inference for Relationships Between Categorical and Quantitative Variables. Inference for a Paired Design with t. Inference for a Two-Sample Design with t. Students Talk Stats: Ordinary Vs. Pooled TwoSample t. Inference for a Several-sample Design with F: Analysis of Variance. Students Talk Stats: Reviewing Relationships between Categorical and Quantitative Variables. 12. Inference for Relationships Between Two Categorical Variables. Comparing Proportions with a z Test. Comparing Counts with a Chi-Square Test. 13. Inference for Relationships Between Two Quantitative Variables. Inference for Regression: Focus on the Slope of the Regression Line. Students Talk Stats: No Evidence of a Relationship. Interval Estimates for an Individual or Mean Response. 14. How Statistics Problems Fit into the Big Picture. The Big Picture in Problem-Solving. Students Talk Stats: Choosing the Appropriate Statistical Tools. 15. Non-Parametric Methods (Online). The Sign Test as an Alternative to the Paired t Test. The Rank-Sum Test as an Alternative to the Two-Sample t Test. Summary of Non-Parametrics. 16. Two-Way ANOVA (Online). 17. Multiple Regression (Online). PART V: SOLUTIONS TO SELECTED EXERCISES. © 2011, 608pp, Paperback, 9780495831457 www.cengageasia.com 5 ELEMENTARY STATISTICS, 11E Robert R. Johnson, Monroe Community College; Patricia J. Kuby In their own classrooms, through their popular texts, and in the conferences they lead, Robert Johnson and Patricia Kuby have inspired hundreds of thousands of students and their instructors to see the utility and practicality of statistics. Now in its Eleventh Edition, ELEMENTARY STATISTICS has been consistently praised by users and reviewers for its clear exposition and relevant examples, exercises, and applications. A focus on technology to help students succeed-including MINITAB®, Excel®, and TI-83/84 output and instructions throughout--is enhanced by a wealth of supplements that save instructors time and give students interactive guidance and support. All this and more have established this text’s reputation for being remarkably accessible for students to learn from--and simple and straightforward for instructors to teach from. NEW TO THIS EDITION • New chapter-opening vignettes--More than 50% of the book’s chapter-opening vignettes, each of which focuses on an everyday aspect of life, are new. Illustrated with statistical information, each chapter opener provides a relevant, familiar context for students’ initial step into the concepts covered in the chapter. • New applied examples--Nearly 20% of the text’s applied examples are new or updated to help engage student interest. Enhanced step-by-step solutions present key statistical concepts in a clear progression. • Over 20% new and updated exercises--21% of the exercises are new or updated to reflect current events and other timely topics that involve the use of statistics. • Chapter 6, “Normal Probability Distributions,” has been completely rewritten to present the Standard Normal Distribution utilizing the cumulative area approach, incorporating a more intuitive idea with respect to the total area under a curve and following more closely the format used with calculators and statistical software. To support this change, a new, corresponding two-page Table 3, “Cumulative Areas of the Standard Normal Distribution,” is included among the tables at the back of the text. • Statistics CourseMate--This new online resource provides a rich array of interactive and supplementary material to accompany the text, including an eBook version of the text. For more information, see the full description under Supplements. • Solution Builder for ELEMENTARY STATISTICS, 11th Edition--This new instructor resource provides fully worked-out solutions to all text exercises in customizable online format. For more information, see the full description under Supplements. • Aplia™ online homework for ELEMENTARY STATISTICS, 11th Edition--Aplia is a premium online homework system dedicated to improving learning by increasing student effort and engagement. For more information, see the full description under Supplements. FEATURES • Emphasis on interpretation of statistical information and real applications--Starting in Chapter 1, when students learn key terms and procedures; in Chapter 4, “Probability,” where analysis rather than formula is highlighted; and continuing throughout the text, the authors emphasize the role of interpretation in statistical analysis. Examples and exercises feature real applications of statistics, and chapter-opening vignettes enhance the relevance of the material for students. Critical thinking exercises throughout chapters further support the book’s practical, proven approach. • Skillbuilder Applet exercises and interactive video skillbuilders--These exercises give students increased opportunities for hands-on learning and shift their attention from simply memorizing formulas and concepts to actively internalizing the behavior of concepts and interpreting data. Created to help students build the foundational skills they need, the online videos provide step-by-step instruction, walking students through key examples from the text. www.cengageasia.com 6 • Flexible organization and integrated, up-to-date technology instructions--The p-value and classical approaches to hypothesis testing are initially introduced separately and thereafter presented sideby-side, to both emphasize their comparability and allow a wide range of teaching approaches. Similarly, descriptive regression and correlation are covered early (Chapter 3), and MINITAB®, Excel®, and the TI-83/84 graphing calculator instructions are found throughout the text instead of being relegated to end-of-chapter material or appendices. CONTENTS Part I: DESCRIPTIVE STATISTICS. 1. Statistics. 2. Descriptive Analysis and Presentation of Single-Variable Data. 3. Descriptive Analysis and Presentation of Bivariate Data. Part II: PROBABILITY. 4. Probability. 5. Probability Distributions (Discrete Variables). 6. Normal Probability Distributions. 7. Sample Variability. Part III: INFERENTIAL STATISTICS. 8. Introduction to Statistical Inferences. 9. Inferences Involving One Population. 10. Inferences Involving Two Populations. Part IV: MORE INFERENTIAL STATISTICS. 11. Applications of ChiSquare. 12. Analysis of Variance. 13. Linear Correlation and Regression. 14. Elements of Nonparametric Statistics. Appendix A: Basic Principles of Counting. Appendix B: Tables. Introductory Concepts. Review Lessons. Answers to Odd-Numbered Exercises. Answers to Chapter Practice Tests. Index for Applications. Index for Computer and Calculator Instructions. Index. © 2012, 832pp, Hardback, 9780538733502 ELEMENTARY STATISTICS, ENHANCED REVIEW EDITION (W/CD-ROM & CENGAGENOW, INFOTRAC® 2-SEM., ILRN® HOMEWORK, PERSONAL TUTOR, INTERNET COMPANION FOR STATISTICS 2-SEM. PAC), 10E Robert R. Johnson, Monroe Community College; Patricia J. Kuby In their own classrooms, through their popular texts, and in the conferences they lead, Bob Johnson and Pat Kuby have inspired hundreds of thousands of students and their instructors to see the utility and practicality of statistics. Robert Johnson and Patricia Kuby’s ELEMENTARY STATISTICS, Tenth Edition has been consistently praised by users and reviewers for its clear exposition and relevant examples, exercises, and applications. Technology enhancements include the addition of Video Skillbuilders and StatisticsNow™ (part of the CengageNOW suite of technology products), our personalized online learning companion. This increased focus on technology to help students succeed, along with the wealth of instructor supplements and flexibility of technology coverage (with MINITAB, Excel, and TI-83 output and instructions throughout) clearly differentiate this text from its competitors as the most accessible text for students to learn from and the most straightforward text for instructors to teach from. NEW TO THIS EDITION • The new introductory concepts, review lessons, and solutions located in the back of the text written by author, Patricia Kuby, provide your students with the help they need to grasp various algebraic and basic statistical concepts. • New Learning Outcomes have been added to the endof-chapter material. These outcomes relate to specific section material and exercises and allow students to www.cengageasia.com 7 diagnose their mastery of a topic. • StatisticsNow™ (part of the CengageNOW suite of technology products). Featured within chapters, StatisticsNow is a robust, personalized online learning companion that helps students gauge their own unique study needs and makes the most of their study time by building focused Personalized Learning Plans that reinforce key concepts. Pre-Tests give students an initial assessment of their knowledge. Personalized Learning Plans, based on the students’ answers to the pre-test questions, outline key elements for review. Post-Tests assess students’ mastery of core concepts for each chapter. Access to StatisticsNow is available at no additional charge with purchase of a new text. • Interactive Video Skillbuilders contain hours of helpful, interactive video instruction. These videos walk your students through key examples from the text, step by stepgiving them a foundation in the skills that they need to know. Video icons located in the margin guide students to view the video on the Skillbuilder CD-ROM. • New technology output has been added throughout the text, reflecting the latest changes to MINITAB, Excel, and TI-83/84 graphing calculator output. • 30% of the exercises are new and many others have been significantly updated. Additional critical thinking exercises have been infused throughout the exercise sets. In addition, Classic Exercises can still be found on the CD-ROM accompanying the text, providing more opportunity to assign practice exercises. CONTENTS Part I: DESCRIPTIVE STATISTICS. 1. Statistics. Americans, Here’’s Looking At You. What is Statistics? Measurability and Variability. Data Collection. Comparison of Probability and Statistics. Statistics and Technology. 2. Descriptive Analysis and Presentation of Single-Variable Data. You and the Internet. Graphical Presentation of Data. Graphs, Pareto Diagrams, and Stem-And-Leaf Displays. Frequency Distributions and Histograms. Numerical Descriptive Statistics. Measures of Central Tendency. Measures of Dispersion. Measures of Position. Interpreting and Understanding Standard Deviation. The Art of Statistical Deception. Mean and Standard Deviation of Frequency Distribution (Optional). 3. Descriptive Analysis and Presentation of Bivariate Data. The Kid is All Grown Up. Bivariate Data. Linear Correlation. Linear Regression. Part II: PROBABILITY. 4. Probability. Sweet Statistics. Probability of Events. Conditional Probability of Events. Rules of Probability. Mutually Exclusive Events. Independent Events. Mutually Exclusive, Independent Events—A Relationship? 5. Probability Distributions (Discrete Variables). Caffeine Drinking. Random Variables. Probability Distribution of a Discrete Random Variable. Mean and Variance of a Discrete Probability Distribution. The Binomial Probability Distribution. Mean and Standard Deviation of the Binomial Distribution. 6. Normal Probability Distributions. Intelligence Scores. Normal Probability Distributions. The Standard Normal Distribution. Applications of Normal Distributions. Notation. Normal Approximation of the Binomial. 7. Sample Variability. 275 Million Americans. Sampling Distributions. The Sampling Distribution of Sample Means. Application of the Sampling Distribution of Sample Means. Part III: INFERENTIAL STATISTICS. 8. Introduction to Statistical Inferences. Were They Shorter Back Then? The Nature of Estimation. Estimation of a Mean (ó known). The Nature of Hypothesis Testing. Hypothesis Test of Mean ì (ó Known): A Probability Value Approach. Hypothesis Test of Mean ì (ó Known): A Classical Approach. 9. Inferences Involving One Population. Get Enough Daily Exercise? Inferences About Mean ì (ó Unknown). Inferences About the Binomial Probability of Success. Inferences About Variance and Standard Deviation. 10. Inferences Involving Two Populations. Students, Credit Cards and Debt. Independent and Dependent Samples. Inferences Concerning the Mean Difference Using Two Dependent Samples. Inferences Concerning the Difference Between Means Using Two Independent Samples. Inferences Concerning the Difference Between Proportions Using Two Independent Samples. Inferences Concerning the Ratio of Variances Using Two Independent Samples. Part IV: MORE INFERENTIAL STATISTICS. 11. Applications of Chi-Square. Cooling a Great Hot Taste. Chi-Square Statistic. Inferences Concerning Multinomial Experiments. Inferences Concerning Contingency Tables. 12. Analysis of Variance. Time Spent Commuting to Work. Introduction to the Analysis of Variance Technique. The Logic Behind ANOVA. Applications of Single-Factor ANOVA. 13. Linear Correlation and Regression. Beautiful Golden Wheat! Linear Correlation Analysis. Inferences About the Linear Correlation Coefficient. Linear Regression Analysis. Inferences Concerning the Slope of the Regression Line. Confidence www.cengageasia.com 8 Interval Estimates For Regression. Understanding the Relationship Between Correlation and Regression. 14. Elements of Nonparametric Statistics. Teenagers’’ Attitudes. Nonparametric Statistics. Comparing Statistical Tests. The Sign Test. The Mann-Whitney U Test. The Runs Test. Rank Correlation. Appendix A: Basic Principles of Counting. Appendix B: Tables. Answers to Odd-Numbered Exercises. Answers to Chapter Practice Tests. Index for Applications. Index for Computer and Calculator Instructions. Index. Credits. Formula Card. © 2008, 832pp, Hardback, 9780495383864 ELEMENTARY STATISTICS, INTERNATIONAL EDITION, 11E Robert R. Johnson, Monroe Community College; Patricia J. Kuby In their own classrooms, through their popular texts, and in the conferences they lead, Robert Johnson and Patricia Kuby have inspired hundreds of thousands of students and their instructors to see the utility and practicality of statistics. Now in its Eleventh Edition, ELEMENTARY STATISTICS, International Edition has been consistently praised by users and reviewers for its clear exposition and relevant examples, exercises, and applications. A focus on technology to help students succeed—including MINITAB®, Excel®, and TI-83/84 output and instructions throughout—is enhanced by a wealth of supplements that save instructors time and give students interactive guidance and support. All this and more have established this text’s reputation for being remarkably accessible for students to learn from—and simple and straightforward for instructors to teach from. NEW TO THIS EDITION • New chapter-opening vignettes—More than 50% of the book’s chapter-opening vignettes, each of which focuses on an everyday aspect of life, are new. Illustrated with statistical information, each chapter opener provides a relevant, familiar context for students’ initial step into the concepts covered in the chapter. • New applied examples—Nearly 20% of the text’s applied examples are new or updated to help engage student interest. Enhanced step-by-step solutions present key statistical concepts in a clear progression. • Over 20% new and updated exercises—21% of the exercises are new or updated to reflect current events and other timely topics that involve the use of statistics.The text’s more than 1700 exercises provide a wealth of practice problems, with each exercise set including a range of exercise types that progress from basic recall to multi-step to items requiring critical thinking. As always, most exercises can be calculated either by hand or by using technology. • Chapter 6, “Normal Probability Distributions,” has been completely rewritten to present the Standard Normal Distribution utilizing the cumulative area approach, incorporating a more intuitive idea with respect to the total area under a curve and following more closely the format used with calculators and statistical software. To support this change, a new, corresponding two-page Table 3, “Cumulative Areas of the Standard Normal Distribution,” is included among the tables at the back of the text. • Statistics CourseMate—This new online resource provides a rich array of interactive and supplementary material to accompany the text, including an eBook version of the text. For more information, see the full description under Supplements. • Solution Builder for ELEMENTARY STATISTICS, 11e, International Edition—This new instructor resource provides fully worked-out solutions to all text exercises in customizable online format. For more information, see the full description under Supplements. • Aplia™ online homework for ELEMENTARY STATISTICS, 11e, International Edition—Aplia is a premium online homework system dedicated to improving learning by increasing student effort and engagement. For more information, see the full description under Supplements. FEATURES • Emphasis on interpretation of statistical information www.cengageasia.com 9 and real applications—Starting in Chapter 1, when students learn key terms and procedures; in Chapter 4, “Probability,” where analysis rather than formula is highlighted; and continuing throughout the text, the authors emphasize the role of interpretation in statistical analysis. Examples and exercises feature real applications of statistics, and chapter-opening vignettes enhance the relevance of the material for students. Critical thinking exercises throughout chapters further support the book’s practical, proven approach. • Skillbuilder Applet exercises and interactive video skillbuilders—These exercises give students increased opportunities for hands-on learning and shift their attention from simply memorizing formulas and concepts to actively internalizing the behavior of concepts and interpreting data. Created to help students build the foundational skills they need, the online videos provide step-by-step instruction, walking students through key examples from the text. • Flexible organization and integrated, up-to-date technology instructions—The p-value and classical approaches to hypothesis testing are initially introduced separately and thereafter presented sideby-side, to both emphasize their comparability and allow a wide range of teaching approaches. Similarly, descriptive regression and correlation are covered early (Chapter 3), and MINITAB®, Excel®, and the TI-83/84 graphing calculator instructions are found throughout the text instead of being relegated to end-of-chapter material or appendices. CONTENTS Part I: DESCRIPTIVE STATISTICS. 1. Statistics. 2. Descriptive Analysis and Presentation of Single-Variable Data. 3. Descriptive Analysis and Presentation of Bivariate Data. Part II: PROBABILITY. 4. Probability. 5. Probability Distributions (Discrete Variables). 6. Normal Probability Distributions. 7. Sample Variability. Part III: INFERENTIAL STATISTICS. 8. Introduction to Statistical Inferences. 9. Inferences Involving One Population. 10. Inferences Involving Two Populations. Part IV: MORE INFERENTIAL STATISTICS. 11. Applications of ChiSquare. 12. Analysis of Variance. 13. Linear Correlation and Regression. 14. Elements of Nonparametric Statistics. Appendix A: Basic Principles of Counting. Appendix B: Tables. Introductory Concepts. Review Lessons. Answers to Odd-Numbered Exercises. Answers to Chapter Practice Tests. Index for Applications. Index for Computer and Calculator Instructions. Index. © 2012, 832pp, Paperback, 9780840058782, Available INTRODUCTION TO PROBABILITY AND STATISTICS, INTERNATIONAL EDITION, 14E William Mendenhall, University of Florida, Emeritus; Robert J. Beaver, University of California, Riverside; Barbara M. Beaver, University of California, Riverside Used by hundreds of thousands of students since its first edition, INTRODUCTION TO PROBABILITY AND STATISTICS, 14E, International Edition continues to blend the best of its proven, error-free coverage with new innovations. Written for the higher end of the traditional introductory statistics market, the book takes advantage of modern technology—including computational software and interactive visual tools—to facilitate statistical reasoning as well as the interpretation of statistical results. In addition to showing how to apply statistical procedures, the authors explain how to describe real sets of data meaningfully, what the statistical tests mean in terms of their practical applications, how to evaluate the validity of the assumptions behind statistical tests, and what to do when statistical assumptions have been violated. The new edition retains the statistical integrity, examples, exercises, and exposition that have made this text a market leader—and builds upon this tradition of excellence with new technology integration. NEW TO THIS EDITION • The book’s website, Statistics CourseMate, includes a Personal Trainer feature that offers further explanation of some of the more difficult concepts through detailed definitions, step-by-step hints on www.cengageasia.com 10 problem solving, self-correcting exercises, and data sets for text exercises. Applets demonstrate key concepts so they are easily visualized, accompanied by exercises that provide practice in the required statistical methods and immediate feedback with answers. An icon in the text margin identifies concepts and applications for which an applet is available on the website. • Technology Today features at the end of each chapter offer Microsoft® Excel 2010 and MINITAB outputs and instructions, giving students hands-on experience performing statistical analysis with these widely used programs. 12. Linear Regression and Correlation. 13. Multiple Regression Analysis. 14. Analysis of Categorical Data. 15. Nonparametric Statistics. Appendix I: Tables. Answers to Selected Exercises. © 2013, 744pp, Paperback, 9789814568890 FEATURES • Exercises: The book includes more than 1,300 exercises, many of which are new or updated for this edition. New topics for the book’s chapter-ending case study exercises include “How to Save Money for Groceries,” “School Accountability Study: How Is Your School Doing?” and “Are You Going to Curve the Grades?” • Market leader: The integrity of the statistics and the quality of the examples and exercises keep this text in the bestseller category. Comprehensive in coverage, it delivers a more rigorous offering with traditional coverage of probability. Instructors and students alike appreciate its error-free material and exercises, and its clear exposition. • Real data: The first to incorporate case studies and real data, this text continues to set the standard. Many examples and exercises use authentic data sets, helping students see the connections between their studies and their lives. • Quick reference: At the end of each chapter, Key Concepts and Formulas sections provide quick reference for students, helping them ensure they are well prepared for assignments and tests. INTRODUCTION TO STATISTICS AND DATA ANALYSIS, 4E Roxy Peck, California Polytechnic State University; Chris Olsen, George Washington High School, Cedar Rapids, Iowa; Jay L. Devore, California Polytechnic State University, San Luis Obispo Roxy Peck, Chris Olsen, and Jay Devore’s new edition uses real data and attention-grabbing examples to introduce students to the study of statistics and data analysis. Traditional in structure yet modern in approach, this text guides students through an intuition-based learning process that stresses interpretation and communication of statistical information. Simple notation--including the frequent substitution of words for symbols--helps students grasp concepts and cement their comprehension. Handson activities and interactive applets allow students to practice statistics firsthand. INTRODUCTION TO STATISTICS AND DATA ANALYSIS, 4th Edition, includes updated coverage of the graphing calculator as well as expanded coverage of probability. NEW TO THIS EDITION CONTENTS Introduction: What Is Statistics? 1. Describing Data with Graphs. 2. Describing Data with Numerical Measures. 3. Describing Bivariate Data. 4. Probability and Probability Distributions. 5. Several Useful Discrete Distributions. 6. The Normal Probability Distribution. 7. Sampling Distributions. 8. Large-Sample Estimation. 9. Large-Sample Tests of Hypotheses. 10. Inference from Small Samples. 11. The Analysis of Variance. • New Cumulative Review Exercises appear after selected chapter sets, allowing you to test students’ comprehension of topics spanning multiple chapters. • More than 50 new examples and more than 270 new exercises, which use data from current newspapers and journals, help students understand statistical concepts in a realistic context. In addition, more of the exercises specifically ask students to write (for www.cengageasia.com 11 example, by requiring them to explain their reasoning, interpret results, and comment on important features of an analysis). • Examples and exercises using data sets that can be accessed online from the text website (Statistics CourseMate) are designated by an icon, as are examples that are further illustrated in technology manuals for MINITAB®, SPSS®, etc.--which are also available at the website. • Exercises have been added to the “Interpreting and Communicating the Results of Statistical Analyses” sections, giving students the chance to practice these important skills. All of these sections now have assignable end-of-section questions associated with them. • Approximately 90 exercises have video solutions, presented by Brian Kotz of Montgomery College, which can be viewed online or downloaded for viewing later. An icon designates these exercises in the text. • A greater number of end-of-chapter activities have been added to the book, ideal for use as a chapter capstone or integrated at appropriate places as the chapter material is covered in class. • The “Graphing Calculator Explorations” have been significantly revised and updated. FEATURES • The text is enhanced by a variety of online teaching and learning resources. These include the book’s website, Statistics CourseMate; Aplia™, an online interactive learning solution that improves comprehension of key statistical concepts; and Cengage Learning’s Enhanced WebAssign®, which allows you to assign problems from the text online and ensure that students receive multimedia tutorial support as they complete their assignments. • Most chapters contain “Graphing Calculator Explorations.” Found at the end of the chapter, these explorations promote statistical thinking and allow students to actively experience technology. • Optional material on logistic regression in Chapter 5, “Summarizing Bivariate Data,” assures complete coverage of this important topic. CONTENTS 1. The Role Of Statistics And The Data Analysis Process. 2. Collecting Data Sensibly. 3. Graphical Methods For Describing Data. 4. Numerical Methods For Describing Data. 5. Summarizing Bivariate Data. 6. Probability. 7. Random Variables And Probability Distributions. 8. Sampling Variability And Sampling Distribution. 9. Estimation Using A Single Sample. 10. Hypothesis Testing Using A Single Sample. 11. Comparing Two Populations Or Treatments. 12. The Analysis Of Categorical Data And Goodness-Of-Fit Tests. 13. Simple Linear Regression And Correlation: Inferential Methods. 14. Multiple Regression Analysis. 15. Analysis Of Variance. 16. Nonparametric (Distribution-Free Statistical Methods (Online). © 2012, 944pp, Hardback, 9780840054906 INTRODUCTION TO STATISTICS AND DATA ANALYSIS, ENHANCED REVIEW EDITION (WITH CENGAGENOW PRINTED ACCESS CARD), 3E Roxy Peck, California Polytechnic State University; Chris Olsen, George Washington High School, Cedar Rapids, Iowa; Jay L. Devore, California Polytechnic State University, San Luis Obispo Roxy Peck, Chris Olsen and Jay Devore’s new edition uses real data and attention-grabbing examples to introduce students to the study of statistics and data analysis. The Third Edition includes coverage of the graphing calculator and includes expanded coverage of probability. Traditional in structure yet modern in approach, this text guides students through an intuition-based learning process that stresses interpretation and communication of statistical information. It helps students grasp concepts and cement their comprehension by using simple notationfrequently substituting words for symbols. Hands-on activities and interactive applets allow students to practice statistics firsthand. This Enhanced Edition includes new Teaching Tips for each chapter in the book, specific references to other available instructor www.cengageasia.com 12 resources, and suggestions for effectively teaching an Advanced Placement Introduction to Statistics course. Also, Enhanced WebAssign now complements a robust supplement package. NEW TO THIS EDITION • Chapter Opening Teaching Tips have been added to beginning of each printed chapter in the Annotated Instructor’s Edition. Broken down section-by-section, these tips suggest additional assignments and activities for a section, as well as the best uses of the supplemental resources for the text (such as the Activity Workbook, Instructor’s Binder and Testbank) that could be included in a lesson plan. • Margin Teaching Tips have been integrated throughout the Annotated Instructor’s Edition. These tips are strategically placed at points where an instructor can integrate an activity, a clarifying example, a quiz to test students on concept formation, and thoughtprovoking questions to ask your students. • Easy to assign. Easy to use. Easy to manage. Enhanced WebAssign allows you to assign, collect, grade, and record homework assignments via the web and includes links to text-specific content, video examples, and problem-specific tutorials. Enchanced WebAssign is more than a homework system; it is a complete learning system for your students. • This text continues to feature broad coverage of sampling, survey design and experimental design coverage of transformations and nonlinear regression; and an emphasis on graphical display as a necessary component of data analysis. CONTENTS 1. The Role Of Statistics And The Data Analysis Process. 2. Collecting Data Sensibly. 3. Graphical Methods For Describing Data. 4. Numerical Methods For Describing Data. 5. Summarizing Bivariate Data. 6. Probability. 7. Random Variables And Probability Distributions. 8. Sampling Variability And Sampling Distributions. 9. Estimation Using A Single Sample. 10. Hypothesis Testing Using A Single Sample. 11. Comparing Two Populations Or Treatments. 12. The Analysis Of Categorical Data And Goodness-Of-Fit Tests. 13. Simple Linear Regression And Correlation: Inferential Methods. 14. Multiple Regression Analysis. 15. Analysis Of Variance. 16. Nonparametric Statistical Methods (Online). © 2009, 880pp, Hardback, 9780495557838 FEATURES • Annotated Instructors Edition – designed specifically to aid you in teaching the course. • “Communicating and Interpreting the Results of Statistical Analysis” sections emphasize the importance of being able to interpret statistical output and communicate its meaning to non-statisticians. • Nearly 100 new examples and hundreds of new or updated exercises with current data from journals, newspapers, and other published sources have been added to this edition, helping students understand statistical concepts in a realistic, relevant context. There are now over 1,100 exercises in the third edition. • Eight new activities bring the total to 33. Found at the ends of chapters, these hands-on activities allow students to see statistics unfold before their eyes as they conduct their own experiments. • New optional material on logistic regression in Chapter 5, “Summarizing Bivariate Data,” assures complete coverage of this important topic. www.cengageasia.com 13 • The number of “In Summary” boxes has been increased, and the boxes are placed more consistently throughout the chapters. A listing of the “In Summary” boxes now appears at the end of the chapter. • The language has been tightened and simplified whenever possible. • The book includes a larger art program to help with visualization. • Significant updates and modifications have been made to Chapter 7 on probability. MIND ON STATISTICS, 5E Jessica M. Utts, University of California, Irvine; Robert F. Heckard, Pennsylvania State University MIND ON STATISTICS, Fifth Edition, emphasizes the conceptual development of statistical ideas and the importance of looking for-and finding-meaning in data. Authors Jessica Utts and Robert Heckard actively engage students’ natural curiosity, motivating them with intriguing questions and explaining statistical topics in the context of a wide range of interesting, useful examples and case studies. Throughout the text, the authors balance the promotion of statistical literacy with coverage of the statistical methodology taught in general introductory statistics courses. Their approach is based on the premises that new material is much easier to learn and remember if it is related to something interesting or previously known, and if students actively ask questions and find the answers for themselves. More than any other text available, MIND ON STATISTICS motivates and encourages students to develop their statistical intuition by focusing on analyzing data and interpreting results, rather than on mathematical formulation. NEW TO THIS EDITION • Increased integration of digital assets. • The book includes a significant number of Skillbuilder applets, which provide opportunities for in-class or independent hands-on exploration of key statistical concepts. The applets can be found on the book’s companion website. • Videos walk students through examples step by step, reinforcing key statistical concepts. • New examples and exercises were written for the new edition. All data in examples, case studies, and exercises also have been updated to the latest information available. FEATURES • To create a more natural flow of topic coverage, Chapters 5 and 6 precede Chapters 3 and 4 so that the material on describing data comes before the two chapters on collecting data. • Topics in this edition include multiple comparisons in Chapters 1 and 13, power curves in Chapters 12 and 13, and McNemar’s test in Chapter 15. CONTENTS 1. Statistics Success Stories and Cautionary Tales. 2. Turning Data into Information. 3. Relationships between Quantitative Variables. 4. Relationships between Categorical Variables. 5. Sampling: Surveys and How to Ask Questions. 6. Gathering Useful Data for Examining Relationships. 7. Probability. 8. Random Variables. 9. Understanding Sampling Distributions: Statistics as Random Variables. 10. Estimating Proportions with Confidence. 11. Estimating Means with Confidence. 12. Testing Hypotheses about Proportions. 13. Testing Hypotheses about Means. 14. Inference about Simple Regression. 15. More about Inference for Categorical Variables. 16. Analysis of Variance. 17. Turning Information into Wisdom. © 2015, 768pp, Hardback, 9781285463186 www.cengageasia.com 14 STAT 2 (WITH REVIEW CARDS AND COURSEMATE PRINTED ACCESS CARD), 2E Robert R. Johnson, Monroe Community College; Patricia J. Kuby Created through a “student-tested, faculty-approved” review process, STAT 2 is an engaging and accessible solution to accommodate the diverse lifestyles of today’s learners at a value-based price. This concise STAT 2 includes all of the key concepts that you require and a full suite of learning aids--including chapter-bychapter self-quizzes and downloadable flash cards--and a wealth of online teaching and learning resources. NEW TO THIS EDITION • Many new real-world examples that students can relate to have been added, such as Teens using Cell Phones in Class, Busy Behind the Wheel, What Employers Look for in Seasonal Employees, and Polishing a Microchip. • New problems have been added at the end of each text chapter, as well as online. • Updated content throughout includes the use of ogives in Chapter 2 and a new method of finding area in Chapter 9. • New Key Terms have been added to many of the chapters. Designed to be easy to spot, these key terms come in handy when students are reviewing the material or preparing for quizzes. • Some of the sections have been reorganized, improving clarity and concept sequencing. • Chapter 4 includes new equations of properties of probability numbers, and an expanded Notes section in the discussion of probability numbers. • Chapter 6 includes a new section on common z values.Specifically, the chapter has been completely rewritten to present the Standard Normal Distribution utilizing the cumulative area approach, incorporating a more intuitive idea with respect to the total area under a curve and following more closely the format used with calculators and statistical software. • Chapter 7 includes a new discussion about the census. • Interested in a simple way to complement your text and course content with study and practice materials? Cengage Learning’s CourseMate brings course concepts to life with interactive learning, study, and exam preparation tools that support the printed textbook. Watch student comprehension soar as your class works with the printed textbook and the textbook-specific website. • Available with this text, Aplia™ is an online interactive learning solution that improves comprehension and outcomes by increasing student effort and engagement. Founded by a professor to enhance his own courses, Aplia provides automatically graded assignments with detailed, immediate explanations on every question, and innovative teaching materials. Our easy-to-use system has been used by more than 1,000,000 students at over 1800 institutions. CONTENTS Part I: DESCRIPTIVE STATISTICS. 1. Statistics. 2. Descriptive Analysis and Presentation of Single-Variable Data. 3. Descriptive Analysis and Presentation of Bivariate Data. Part II: PROBABILITY. 4. Probability. 5. Probability Distributions (Discrete Variables). 6. Normal Probability Distributions. 7. Sample Variability. Part III: INFERENTIAL STATISTICS. 8. Introduction to Statistical Inferences. 9. Inferences Involving One Population. 10. Inferences Involving Two Populations. Part IV: MORE INFERENTIAL STATISTICS. 11. Applications of ChiSquare. 12. Analysis of Variance. 13. Linear Correlation and Regression. 14. Elements of Nonparametric Statistics. © 2012, 368pp, Paperback, 9780538738415 www.cengageasia.com 15 STATISTICS, 7E The Exploration & Analysis of Data Roxy Peck, California Polytechnic State University; Jay L. Devore, California Polytechnic State University, San Luis Obispo Roxy Peck and Jay Devore’s STATISTICS: THE EXPLORATION AND ANALYSIS OF DATA, 7th Edition uses real data and attention-grabbing examples to introduce students to the study of statistics and data analysis. Traditional in structure yet modern in approach, this text guides students through an intuition-based learning process that stresses interpretation and communication of statistical information. Simple notation--including the frequent substitution of words for symbols--helps students grasp concepts and cement their comprehension. Hands-on activities and interactive applets allow students to practice statistics firsthand. NEW TO THIS EDITION • New Cumulative Review Exercises appear after selected chapter sets, allowing you to test students’ comprehension of topics spanning multiple chapters. • More than 50 new examples and more than 270 new exercises, which use data from current newspapers and journals, help students understand statistical concepts in a realistic context. In addition, more of the exercises specifically ask students to write (for example, by requiring students to explain their reasoning, interpret results, and comment on important features of an analysis). • Examples and exercises using data sets that can be accessed online from the text website (Statistics CourseMate) are designated by an icon, as are examples that are further illustrated in technology manuals for MINITAB®, SPSS®, etc.--which are also available at the website. • Exercises have been added to the “Interpreting and Communicating the Results of Statistical Analyses” sections, giving students the chance to practice these important skills. All of these sections now have assignable end-of-section questions associated with them. • More than 90 exercises have video solutions, presented by Brian Kotz of Montgomery College, which can be viewed online or downloaded for viewing later. An icon designates these exercises in the text. • A greater number of end-of-chapter activities have been added to the book, ideal for use as a chapter capstone or integrated at appropriate places as the chapter material is covered in class. • The text is enhanced by a variety of online teaching and learning resources. These include the book’s website, Statistics CourseMate; Aplia™, an online interactive learning solution that improves comprehension of key statistical concepts; and Cengage Learning’s Enhanced WebAssign®, which allows you to assign problems from the text online and ensure that students receive multimedia tutorial support as they complete their assignments. FEATURES • An optional section on logistic regression is included in Chapter 5, “Summarizing Bivariate Data,” for those who would like more complete coverage of data analysis techniques for categorical data. • Advanced topics that are often omitted in a onequarter or one-semester course, such as inference and variable selection methods in multiple regression and analysis of variance for randomized block and two-factor designs, are available online at the book’s website, Statistics CourseMate. • “Interpreting and Communicating the Results of Statistical Analysis” sections, which emphasize the importance of being able to interpret statistical output and communicate its meaning to nonstatisticians, now have assignable end-of-section questions associated with them. CONTENTS 1. THE ROLE OF STATISTICS AND THE DATA ANALYSIS PROCESS. Why Study Statistics. The Nature and Role of Variability. Statistics and the Data Analysis Process. Types of Data and Some Simple Graphical Displays. 2. COLLECTING DATA SENSIBLY. Statistical Studies: Observation and Experimentation. Sampling. Simple www.cengageasia.com 16 Comparative Experiments. More on Experimental Design. More on Observational Studies: Designing Surveys (Optional). Interpreting and Communicating the Results of Statistical Analyses. 3. GRAPHICAL METHODS FOR DESCRIBING DATA. Displaying Categorical Data: Comparative Bar Charts and Pie Charts. Displaying Numerical Data: Stem-and-Leaf Displays. Displaying Numerical Data: Frequency Distributions and Histograms. Displaying Bivariate Numerical Data. Interpreting and Communicating the Results of Statistical Analyses. 4. NUMERICAL METHODS FOR DESCRIBING DATA. Describing the Center of a Data Set. Describing Variability in a Data Set. Summarizing a Data Set: Boxplots. Interpreting Center and Variability: Chebyshev’s Rule, the Empirical Rule, and z Scores. Interpreting and Communicating the Results of Statistical Analyses. 5. SUMMARIZING BIVARIATE DATA. Correlation. Linear Regression: Fitting a Line to Bivariate Data. Assessing the Fit of a Line. Nonlinear Relationships and Transformations. Logistic Regression (Optional). Interpreting and Communicating the Results of Statistical Analyses. 6. PROBABILITY. Interpreting Probabilities and Basic Probability Rules. Probability as a Basis for Making Decisions. Estimating Probabilities Empirically and by Using Simulation. 7. RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS. Describing the Distribution of Values in a Population. Population Models for Continuous Numerical Variables. Normal Distributions. Checking for Normality and Normalizing Transformations. 8. SAMPLING VARIABILITY AND SAMPLING DISTRIBUTION. Statistics and Sampling Variability. The Sampling Distribution of a Sample Mean. The Sampling Distribution of a Sample Proportion. 9. ESTIMATION USING A SINGLE SAMPLE. Point Estimation. Large-Sample Confidence Interval for a Population Proportion. Confidence Interval for a Population Mean. Interpreting and Communicating the Results of Statistical Analyses. 10. HYPOTHESIS TESTING USING A SINGLE SAMPLE. Hypotheses and Test Procedures. Errors in Hypotheses Testing. Large-Sample Hypothesis Tests for a Population Proportion. Hypotheses Tests for a Population Mean. Power and Probability of Type II Error. Interpreting and Communicating the Results of Statistical Analyses. 11. COMPARING TWO POPULATIONS OR TREATMENTS. Inferences Concerning the Difference Between Two Population or Treatment Means Using Independent Samples. Inferences Concerning the Difference Between Two Population or Treatment Means Using Paired Samples. Large Sample Inferences Concerning a Difference Between Two Population or Treatment Proportions. Interpreting and Communicating the Results of Statistical Analyses. 12. THE ANALYSIS OF CATEGORICAL DATA AND GOODNESS-OF-FIT TESTS. Chi-Square Tests for Univariate Data. Tests for Homogeneity and Independence in a Two-way Table. Interpreting and Communicating the Results of Statistical Analyses. 13. SIMPLE LINEAR REGRESSION AND CORRELATION: INFERENTIAL METHODS. Simple Linear Regression Model. Inferences About the Slope of the Population Regression Line. Checking Model Adequacy. Inferences Based on the Estimated Regression Line (Optional). Inferences About the Population Correlation Coefficient (Optional). Interpreting and Communicating the Results of Statistical Analyses. 14. MULTIPLE REGRESSION ANALYSIS. Multiple Regression Models. Fitting a Model and Assessing Its Utility. Inferences Based on an Estimated Model (online). Other Issues in Multiple Regression (online). Interpreting and Communicating the Results of Statistical Analyses (online). 15. ANALYSIS OF VARIANCE. Single-Factor ANOVA and the F Test. Multiple Comparisons. The F Test for a Randomized Block Experiment (online). Two-Factor ANOVA (online). Interpreting and Communicating the Results of Statistical Analyses (online). 16. NONPARAMETRIC (DISTRIBUTION-FREE STATISTICAL METHODS (ONLINE). Distribution-Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Independent Samples (Optional). Distribution-Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Paired Samples. DistributionFree ANOVA. © 2012, 816pp, Hardback, 9780840058010 www.cengageasia.com 17 STATISTICS, INTERNATIONAL EDITION, 10E Charles Henry Brase, Regis University; Corrinne Pellillo Brase, Arapahoe Community College STATISTICS, 10e, International Edition is a thorough, yet accessible program designed to help students overcome their apprehensions about statistics. The authors provide clear guidance and informal advice while showing students the links between statistics and the world. To reinforce this approach, the book integrates real-life data from a variety of sources, including journals, periodicals, newspapers, and the Internet. The Tenth Edition continues to address the importance of developing students’ critical thinking and statistical literacy skills through special features and exercises throughout the text. The use of graphing calculators, Excel®, MINITAB®, and SPSS® is covered though not required. Extensive technology resources include an algorithmic test bank and lecture slides, along with interactive online resources and a marketleading DVD series designed to provide reinforcement for students and support for instructors. NEW TO THIS EDITION • Updated technology-related examples include instruction appropriate for the latest versions of MINITAB®, SPSS®, and Excel®. Denoted by icons in the text, “Tech Notes!” provide best practices and accompanying screenshots to assist students who are using these programs. • Updated real-world applications throughout the text include new examples from a variety of disciplines. • A new “Foreshadowing” feature alerts students to additional related material to be presented in subsequent “Expand Your Knowledge” problems. Uniform probability distribution and the exponential probability distribution are introduced in the “Expand Your Knowledge” section of Chapter 6, “Normal Distributions.” Polynomial regression (curvilinear regression) is discussed in the “Expand Your Knowledge” section of Chapter 9, “Correlation and Regression.” • A new “Looking Ahead” feature gives students a taste of forthcoming topics--showing them how the concepts and skills they’re reading about will be useful in helping them grasp material covered later. • New problems featuring basic computation and using small data sets give students the chance to appreciate the formulas and mathematical processes that their calculators accomplish for them--and help them to better understand what the end result means. • An updated Chapter 6, “Normal Distributions,” now covers this topic as well as sampling distributions to streamline the course and provide an immediate, important application of the normal distribution. The chapter also covers the normal approximation to the binomial distribution. • Instructions for using the TI-Nspire calculator are included in the TI technology guide (available online). • Updated and revised examples in Chapter 4, “Probability,” incorporate sample spaces that are small and easy for students to visualize. • Examples, guided exercises, and problem sets throughout the text feature interpretation. They also address the suitability of using a specified statistical method or process for analysis. FEATURES • The text is enhanced by a variety of online teaching and learning resources. These include the book’s website, Statistics CourseMate; Aplia™, an online interactive learning solution that improves comprehension of key statistical concepts; and Cengage Learning’s Enhanced WebAssign®, which allows you to assign problems from the text online and ensure that students receive multimedia tutorial support as they complete their assignments. CONTENTS Note: Each chapter concludes with a Summary, Important Words and Symbols, Chapter Review Problems, Data Highlights: Group Projects, Linking Concepts: Writing Projects, and Using Technology. Table of Prerequisite Material. 1. GETTING STARTED. Focus Problem: Where Have All the Fireflies Gone? www.cengageasia.com 18 What Is Statistics. Random Samples. Introduction to Experimental Design. 2. ORGANIZING DATA. Focus Problem: Say It with Pictures. Frequency Distributions, Histograms, and Related Topics. Bar Graphs, Circle Graphs, and Time-Series Graphs. Stem-and-Leaf Displays. 3. AVERAGES AND VARIATION. Focus Problem: The Educational Advantage. Measures of Central Tendency: Mode, Mediate, and Mean. Measures of Variation. Percentiles and Box-andWhisker Plots. Cumulative Review Problems: Chapters 1–3. 4. ELEMENTARY PROBABILITY THEORY. Focus Problem: How Often Do Lie Detectors Lie? What Is Probability? Some Probability Rules—Compound Events. Trees and Counting Techniques. 5. THE BINOMIAL PROBABILITY DISTRIBUTION AND RELATED TOPICS. Focus Problem: Personality Preference Types: Introvert or Extrovert? Introduction to Random Variables and Probability Distributions. Binomial Probabilities. Additional Properties of the Binomial Distribution. The Geometric and Poisson Probability Distributions. 6. NORMAL DISTRIBUTIONS. Focus Problem: Impulse Buying. Graphs of Normal Probability Distributions. Standard Units and Areas Under the Standard Normal Distribution. Areas Under Any Normal Curve. Normal Approximation to the Binomial Distribution. Sampling Distributions. The Central Limit Theorem. Cumulative Review Problems: Chapters 4-6. 7. ESTIMATION. Focus Problem: The Trouble Is Unknown.Is Known. Estimating µ When  with Wood Ducks. Estimating µ When Estimating p in the Binomial Distribution. Estimating µ 1-µ 2 and p1-p2. 8. HYPOTHESIS TESTING. Focus Problem: Benford’s Law: The Importance of Being Number 1. Introduction to Statistical Tests. Testing the Mean µ. Testing a Proportion p. Tests Involving Paired Differences (Dependent Samples). Testing µ1-µ2 and p1-p2 (Independent Samples). 9. CORRELATION AND REGRESSION. Focus Problem: Changing Populations and Crime Rate. Scatter Diagrams and Linear Correlation. Linear Regression and the Coefficient of Determination. Inferences for Correlation and Regression. Multiple Regression. Cumulative Review Problems: Chapters 7-9. 10. CHI-SQUARE AND F DISTRIBUTIONS. Focus Problem: Archaeology in Bandelier National Monument. Part I: Inferences Using the Chi-Square Distribution. Overview of the Chi-Square Distribution. Chi-Square: Tests of Independence and of Homogeneity. ChiSquare: Goodness of Fit. Testing and Estimating a Single Variance or Standard Deviation. Part II: Inferences Using the F Distribution. Testing Two Variances. OneWay ANOVA: Comparing Several Sample Means. Introduction to Two-Way ANOVA. 11. NONPARAMETRIC STATISTICS. Focus Problem: How Cold? Compared to What? The Sign Test for Matched Pairs. The RankSum Test. Spearman Rank Correlation. Runs Test for Randomness. Cumulative Review Problems: Chapters 10-12. APPENDIX I: ADDITIONAL TOPICS. Bayes’s Theorem. The Hypergeometric Probability Distribution. APPENDIX II: TABLES. Random Numbers. Binomial Coefficients. Binomial Probability Distribution. Poisson Probability Distribution. Areas of a Standard Normal Distribution. Critical Values for Student’s t Distribution. The X2 Distribution. Critical Values for F Distribution. Critical Values for Spearman Rank Correlation. Critical Values for Number of Runs R. © 2012, 832pp, Paperback, 9781111570040 STATISTICS, INTERNATIONAL EDITION, 4E Robert F. Heckard, Pennsylvania State University; Jessica M. Utts, University of California, Irvine STATISTICS, 4e, International Edition emphasizes the conceptual development of statistical ideas and the importance of looking for—and finding—meaning in data. Authors Jessica Utts and Robert Heckard actively engage students’ natural curiosity, motivating them with intriguing questions and explaining statistical topics in the context of a wide range of interesting, useful examples and case studies. Throughout, the authors balance the promotion of statistical literacy with coverage of the statistical methodology taught in general introductory statistics courses. Their approach is based on the premises that new material is much easier to learn and remember if it is related www.cengageasia.com 19 to something interesting or previously known, and if students actively ask questions and find the answers for themselves. More than any other text available, STATISTICS, 4e, International Edition motivates and encourages students to develop their statistical intuition by focusing on analyzing data and interpreting results, rather than on mathematical formulation. CourseSmart goes beyond traditional expectations– providing instant, online access to the textbooks and course materials you need and at a lower cost to your students. To request an electronic sample of this Cengage Learning title, go to: www.coursesmart.com/ instructors. NEW TO THIS EDITION • To create a more natural flow of topic coverage, Chapters 5 and 6 have been moved to precede Chapters 3 and 4 so that the material on describing data comes before the two chapters on collecting data. • The language has been tightened and simplified in all chapters whenever possible. In particular, Chapters 9, 10, and 12 have been substantially honed. • New topics in this edition include multiple comparisons in Chapters 1 and 13, power curves in Chapters 12 and 13, and McNemar’s test in Chapter 15. • The exercise sets have been significantly reorganized and rewritten. For example, in response to reviewers’ suggestions, many exercises are now set up in oddeven pairs, featuring odd-numbered problems--which are answered in the back of the book--followed by similar even-numbered problems. In addition, a number of new problems have been added, including a significant number of new drill exercises. • The data in the problem sets, examples, and case studies have been updated with the latest possible information. In addition, the book includes all-new examples and case studies as well as new and updated data sets. • A greater number of In Summary boxes are placed consistently throughout each chapter, reinforcing key ideas as students read. A listing of the In Summary boxes now appears at the end of the chapter, making these features even more helpful as study aids. • Aplia™ is available with the new edition. This online interactive learning solution--which improves student comprehension and outcomes with detailed, immediate explanations--also saves you time by automatically grading assignments. FEATURES • Chapters 9–13, containing the core material on sampling distributions and statistical inference, are organized in a modular, flexible format. There are six modules for each of the topics of sampling distributions, confidence intervals, and hypothesis testing. The first module provides an introduction and each of the remaining five modules deals with a specific parameter (one mean, one proportion, etc.). This modular format emphasizes the similarity among the inference procedures for the five parameters discussed, and allows instructors to cover this material in any order they choose. • Technology Tips appear throughout the text for MINITAB®, SPSS®, Excel®, JMP, and TI-83/84 calculators. Technical manuals for these technologies, as well as for JMP and R, are available on the book’s website, Statistics CourseMate. • Original journal articles for selected case studies and examples (identified in the text by an icon) can be found on the book’s website, Statistics CourseMate. Reading these original articles enables students to learn more about how the research was conducted, what methods were used, and the conclusions drawn. CONTENTS 1. STATISTICS SUCCESS STORIES AND CAUTIONARY TALES. What Is Statistics? Eight Statistical Stories with Morals. The Common Elements in the Eight Stories. 2. TURNING DATA INTO INFORMATION. Raw Data. Types of Variables. Summarizing One or Two Categorical Variables. Exploring Features of Quantitative Data with Pictures. Numerical Summaries of Quantitative Variables. How to Handle Outliers. Bell-Shaped Distributions and Standard Deviations. Skillbuilder Applet: The Empirical Rule in Action. 3. RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES. Looking for Patterns with Scatterplots. Describing Linear Patterns with a Regression Line. Measuring Strength and Direction with Correlation. Regression and Correlation Difficulties and Disasters. Correlation Does Not Prove Causation. Skillbuilder Applet: Exploring Correlation. 4. RELATIONSHIPS BETWEEN CATEGORICAL VARIABLES. Displaying Relationships Between Categorical Variables. Risk, Relative Risk, and Misleading Statistics About Risk. The Effect of a Third Variable and Simpson’s www.cengageasia.com 20 Paradox. Assessing the Statistical Significance of a 2 × 2 Table. 5. SAMPLING: SURVEYS AND HOW TO ASK QUESTIONS. Collecting and Using Sample Data Wisely. Margin of Error, Confidence Intervals, and Sample Size. Choosing a Simple Random Sample. Other Sampling Methods. Difficulties and Disasters in Sampling. How to Ask Survey Questions. Skillbuilder Applet: Random Sampling in Action. 6. GATHERING USEFUL DATA FOR EXAMINING RELATIONSHIPS. Speaking the Language of Research Studies. Designing a Good Experiment. Designing a Good Observational Study. Difficulties and Disasters in Experiments and Observational Studies. 7. PROBABILITY. Random Circumstances. Interpretations of Probability. Probability Definitions and Relationships. Basic Rules for Finding Probabilities. Finding Complicated Probabilities. Using Simulation to Estimate Probabilities. Flawed Intuitive Judgments About Probability. 8. RANDOM VARIABLES. What Is a Random Variable? Discrete Random Variables. Expectations for Random Variables. Binomial Random Variables. Continuous Random Variables. Normal Random Variables. Approximating Binomial Distribution Probabilities. Sums, Differences, and Combinations of Random Variables. 9. UNDERSTANDING SAMPLING DISTRIBUTIONS: STATISTICS AS RANDOM VARIABLES. Parameters, Statistics, and Statistical Inference. From Curiosity to Questions About Parameters. SD Module 0: An Overview of Sampling Distributions. SD Module 1: Sampling Distribution for One Sample Proportion. SD Module 2: Sampling Distribution for the Difference in Two Sample Proportions. SD Module 3: Sampling Distribution for One Sample Mean. SD Module 4: Sampling Distribution for the Sample Mean of Paired Differences. SD Module 5: Sampling Distribution for the Difference in Two Sample Means. Preparing for Statistical Inference: Standardized Statistics. Generalizations Beyond the Big Five. Skillbuilder Applet: Finding the Pattern in Sample Means. 10. ESTIMATING PROPORTIONS WITH CONFIDENCE. CI Module 0: An Overview of Confidence Intervals. CI Module 1: Confidence Interval for a Population Proportion. CI Module 2: Confidence Intervals for the Difference in Two Population Proportions. Using Confidence Intervals to Guide Decisions. 11. ESTIMATING MEANS WITH CONFIDENCE. Introduction to Confidence Intervals for Means. CI Module 3: Confidence Interval for One Population Mean. CI Module 4: Confidence Interval for the Population Mean of Paired Differences. CI Module 5: Confidence Interval for the Difference in Two Population Means (Independent Samples). Understanding Any Confidence Interval. Skillbuilder Applet: The Confidence Level in Action. 12. TESTING HYPOTHESES ABOUT PROPORTIONS. HT Module 0: An Overview of Hypothesis Testing. HT Module 1: Testing Hypotheses About a Population Proportion. HT Module 2: Testing Hypotheses About the Difference in Two Population Proportions. Sample Size, Statistical Significance, and Practical Importance. 13. TESTING HYPOTHESES ABOUT MEANS. Introduction to Hypothesis Tests for Means. HT Module 3: Testing Hypotheses about One Population Mean. HT Module 4: Testing Hypotheses about the Population Mean of Paired Differences. HT Module 5: Testing Hypotheses about the Difference in Two Population Means (Independent Samples). The Relationship Between Significance Tests and Confidence Intervals. Choosing an Appropriate Inference Procedure. Effect Size. Evaluating Significance in Research Reports. 14. INFERENCE ABOUT SIMPLE REGRESSION. Sample and Population Regression Models. Estimating the Standard Deviation for Regression. Inference About the Slope of a Linear Regression. Predicting y and Estimating Mean y at a Specific x. Checking Conditions for Using Regression Models for Inference. 15. MORE ABOUT INFERENCE FOR CATEGORICAL VARIABLES. The Chi-Square Test for Two-Way Tables. Analyzing 2 × 2 Tables. Testing Hypotheses About One Categorical Variable: Goodness-of-Fit. 16. ANALYSIS OF VARIANCE. Comparing Means with an ANOVA F-Test. Details of One-Way Analysis of Variance. Other Methods for Comparing Populations. Two-Way Analysis of Variance. 17. TURNING INFORMATION INTO WISDOM. Beyond the Data. Transforming Uncertainty Into Wisdom. Making Personal Decisions. Control of Societal Risks. Understanding Our World. Getting to Know You. Words to the Wise. © 2012, 800pp, Paperback, 9780538735919 www.cengageasia.com 21 Statistics Intermediate/Advanced Statistics life scenarios drawn from a variety of disciplines, including agriculture, business, economics, education, engineering, medicine, law, political science, psychology, environmental studies, and sociology. • An expanded discussion of the proper methods for designing studies and experiments provides students with additional information to help them analyze and evaluate published research results and to plan and conduct their own research more effectively. FEATURES AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, 6E R. Lyman Ott; Michael T. Longnecker Ott and Longnecker’s AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Sixth Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and in news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments. NEW TO THIS EDITION • Every chapter includes a case study to help students appreciate the role statistics plays in the solution of practical, real-world problems. Case studies are selected from a variety of sources to ensure broad reader appeal, and all feature the same four-step process used in the text to facilitate connections to the chapter material and reinforce learning. • Expanded and updated exercises at the end of every chapter engage readers by exploring real- • The text emphasizes step-by-step learning and practical skill-building by providing frequent opportunities for students to interpret results and draw conclusions from studies that illustrate key concepts, providing an immediate, effective way to review and apply the material. • A uniquely effective Four-Step Process to ProblemSolving and Understanding the Collected Data, developed by the authors, helps students of all backgrounds master the essentials of statistics by 1) gathering data, 2) summarizing data, 3) analyzing data, and 4) communicating the results of data analyses. • Computer output from Minitab®, SAS, and SPSS is provided in numerous examples and exercises to familiarize students with the use of more sophisticated graphical illustrations of statistical results. • A special section titled “What is Statistics?” opens the text by explaining why students should study statistics and presenting an engaging discussion of several major studies illustrating the use of statistics to solve a variety of important real-life problems. CONTENTS PART 1: INTRODUCTION. 1. Statistics and the Scientific Method. PART 2: COLLECTING DATA. 2. Using Surveys and Scientific Studies to Collect Data. PART 3: SUMMARIZING DATA. 3. Data Description. Introduction and Abstract of Research Study. 4. Probability And Probability Distributions. PART 4: ANALYZING DATA, INTERPRETING THE ANALYSES, AND COMMUNICATING RESULTS. 5. Inferences about Population Central Values. 6. Inferences Comparing Two Population Central Values. 7. Inferences about Population Variances. 8. Inferences About More Than Two Population Central Values Introduction and Abstract of a Research Study. 9. Multiple Comparisons. Introduction and Abstract of Research Study. PART www.cengageasia.com 22 5: ANALYZING DATA: REGRESSION METHODS AND MODEL BUILDING. 11. Linear Regression and Correlation. 12. Multiple Regression and the General Linear Model. 13. Further Regression Topics. PART 6: DESIGN OF EXPERIMENTS AND ANALYSIS OF VARIANCE. 14. Analysis of Variance for Completely Randomized Designs. 15. Analysis of Variance for Blocked Designs. 16. Analysis of Covariance. 17. Analysis of Variance for Some Fixed-, Random-, and MixedEffects Models. 18. Split-Plot, Repeated Measures, and Crossover Designs. 19. Analysis of Variance for Some Unbalanced Designs. PART 7: COMMUNICATING AND DOCUMENTING THE RESULTS OF ANALYSES 20. Communicating and Documenting the Results of a Study or Experiment. © 2010, 1296pp, Hardback, 9780495017585 AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, INTERNATIONAL EDITION, 6E NEW TO THIS EDITION • Every chapter includes a case study to help students appreciate the role statistics plays in the solution of practical, real-world problems. Case studies are selected from a variety of sources to ensure broad reader appeal, and all feature the same four-step process used in the text to facilitate connections to the chapter material and reinforce learning. • The text emphasizes step-by-step learning and practical skill-building by providing frequent opportunities for students to interpret results and draw conclusions from studies that illustrate key concepts, providing an immediate, effective way to review and apply the material. • Expanded and updated exercises at the end of every chapter engage readers by exploring reallife scenarios drawn from a variety of disciplines, including agriculture, business, economics, education, engineering, medicine, law, political science, psychology, environmental studies, and sociology. • An expanded discussion of the proper methods for designing studies and experiments provides students with additional information to help them analyze and evaluate published research results and to plan and conduct their own research more effectively. FEATURES R. Lyman Ott; Michael T. Longnecker Ott and Longnecker’s AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, 6th Edition, International Edition provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and in news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments. • A uniquely effective Four-Step Process to ProblemSolving and Understanding the Collected Data, developed by the authors, helps students of all backgrounds master the essentials of statistics by 1) gathering data, 2) summarizing data, 3) analyzing data, and 4) communicating the results of data analyses. • Computer output from Minitab®, SAS, and SPSS is provided in numerous examples and exercises to familiarize students with the use of more sophisticated graphical illustrations of statistical results. • A special section titled “What is Statistics?” opens the text by explaining why students should study statistics and presenting an engaging discussion of several major studies illustrating the use of statistics to solve a variety of important real-life problems. CONTENTS www.cengageasia.com 23 Part 1: INTRODUCTION. 1. Statistics and the Scientific Method. Introduction. Why Study Statistics? Some Current Applications of Statistics. A Note to the Student. Summary. Exercises. Part 2: COLLECTING DATA. 2. Using Surveys and Scientific Studies to Collect Data. Introduction and Abstract of Research Study. Observational Studies. Sampling Designs for Surveys. Experimental Studies. Designs for Experimental Studies. Research Study: Exit Polls versus Election Results. Summary. Exercises. Part 3: SUMMARIZING DATA. 3. Data Description. Introduction and Abstract of Research Study. Calculators, Computers, and Software Systems. Describing Data on a Single Variable: Graphical Methods. Describing Data on a Single Variable: Measures of Central Tendency. Describing Data on a Single Variable: Measures of Variability. The Boxplot. Summarizing Data from More Than One Variable: Graphs and Correlation. Research Study: Controlling for Student Background in the Assessment of Teaching. Summary and Key Formulas. Exercises. 4. Probability And Probability Distributions. Introduction and Abstract of Research Study. Finding the Probability of an Event. Basic Event Relations and Probability Laws. Conditional Probability and Independence. Bayes’ Formula. Variables: Discrete and Continuous. Probability Distributions for Discrete Random Variables. Two Discrete Random Variables: The Binomial and the Poisson. Probability Distributions for Continuous Random Variables. A Continuous Probability Distribution: The Normal Distribution. Random Sampling. Sampling Distributions. Normal Approximation to the Binomial. Evaluating Whether or Not a Population Distribution Is Normal. Research Study: Inferences about Performance Enhancing Drugs among Athletes. Minitab Instructions. Summary and Key Formulas. Exercises. Part 4: ANALYZING DATA, I N T E R P R E T I N G T H E A N A LY S E S , A N D COMMUNICATING RESULTS. 5. Inferences about Population Central Values. Introduction and Abstract of a Research Study. Estimation of μ. Choosing the Sample Size for Estimating μ. A Statistical Test for μ. Choosing the Sample Size for μ. The Level of Significance of a Statistical Test. Inferences about μ for a Normal Population, σ Unknown. Inferences about μ when Population in Nonnormal and n is small: Bootstrap Methods. Inferences about the Median. Research Study: Percent Calories from Fat. Summary and Key Formulas. Exercises. 6. Inferences Comparing Two Population Central Values. Introduction and Abstract of a Research Study. Inferences about μ1 - μ2: Independent Samples. A Nonparametric Alternative: The Wilcoxon Rank Sum Test. Inferences about μ1 - μ2: Paired Data. A Nonparametric Alternative: The Wilcoxon Signed-Rank Test. Choosing Sample Sizes for Inferences about μ1 - μ2. Research Study: Effects of Oil Spill on Plant Growth. Summary. Exercises. 7. Inferences about Population Variances. Introduction and Abstract of a Research Study. Estimation and Tests for a Population Variance. Estimation and Tests for Comparing Two Population Variances. Tests for Comparing t > 2 Population Variances. Research Study: Evaluation of Methods for Detecting E. coli. Summary and Key Formulas. Exercises. 8. Inferences About More Than Two Population Central Values. Introduction and Abstract of a Research Study. A Statistical Test About More Than Two Population Means: An Analysis of Variance. The Model for Observations in a Completely Randomized Design. Checking on the AOV Conditions. An Alternative Analysis: Transformations of the Data. A Nonparametric Alternative: The Kruskal-Wallis Test. Research Study: Effect on Timing on the Treatment of Port-Wine Stains with Lasers. Summary and Key Formulas. Exercises. 9. Multiple Comparisons. Introduction and Abstract of Research Study. Linear Contrasts. Which Error Rate Is Controlled? Fisher’s Least Significant Difference. Tukey’s W Procedure. Student-Neuman-Keuls Procedure. Dunnett’s Procedure: Comparison of Treatments to a Control. Scheffé’s S Method. A Nonparametric MultipleComparison Procedure. Research Study: Are Interviewers’ Decisions Affected by Different Handicap Types? Summary and Key Formulas. Exercises. 10. Categorical Data. Introduction and Abstract of Research Study. Inferences about a Population Proportion π. Inferences about the Difference between Two Population Proportions, π1 - π2. Inferences about Several Proportions: Chi-Square Goodness-of-Fit Test. Tests for Independence and Homogeneity. Measuring Strength of Relaxation. Odds and Odd Ratios. Combining Sets of 2 X 2 Contingency Tables (optional). Research Study: Does Gender Bias Exist in the Selection of Students for Vocational Education? Summary and Key Formulas. Exercises. Part 5: ANALYZING DATA: REGRESSION METHODS AND MODEL BUILDING. 11. Linear Regression and Correlation. Introduction and Abstract www.cengageasia.com 24 of Research Study. Estimating Model Parameters. Inferences about Regression Parameters. Predicting New y Values Using Regression. Examining Lack of Fit in Linear Regression. The Inverse Regression Problem (Calibration). Correlation. Research Study: Two Methods for Detecting E. coli. Summary and Key Formulas. Exercises. 12. Multiple Regression and the General Linear Model. Introduction and Abstract of Research Study. The General Linear Model. Estimating Multiple Regression Coefficients. Inferences in Multiple Regression. Testing a Subset of Regression Coefficients. Forecasting Using Multiple Regression. Comparing the Slopes of Several Regression Lines. Logistic Regression. Some Multiple Regression Theory (Optional). Research Study: Designing an Electric Drill. Summary and Key Formulas. Exercises. 13. Further Regression Topics. Introduction and Abstract of Research Study. Selecting the Variables (Step 1). Formulating the Model (Step 2). Checking Model Assumptions (Step 3). Research Study: Construction Costs for Nuclear Power Plants. Summary and Key Formulas. Exercises. Part 6: DESIGN OF EXPERIMENTS AND ANALYSIS OF VARIANCE. 14. Analysis of Variance for Completely Randomized Designs. Introduction and Abstract of Research Study. Completely Randomized Design with Single Factor. Factorial Treatment Structure. Factorial Treatment Structures with an Unequal Number of Replications. Estimation of Treatment Differences and Comparisons of Treatment Means. Determining the Number of Replications. Research Study: Development of a Low-Fat Processed Meat. Summary and Key Formulas. Exercises. 15. Analysis of Variance for Blocked Designs. Introduction and Abstract of Research Study. Randomized Complete Block Design. Latin Square Design. Factorial Treatment Structure in a Randomized Complete Block Design. A Nonparametric Alternative—Friedman’s Test. Research Study: Control of Leatherjackets. Summary and Key Formulas. Exercises. 16. Analysis of Covariance. Introduction and Abstract of Research Study. A Completely Randomized Design with One Covariate. The Extrapolation Problem. Multiple Covariates and More Complicated Designs. Research Study: Evaluations of Cool-Season Grasses for Putting Greens. Summary. Exercises. 17. Analysis of Variance for Some Fixed-, Random-, and Mixed-Effects Models. Introduction and Abstract of Research Study. A One-Factor Experiment with Random Treatment Effects. Extensions of Random- Effects Models. Mixed-Effects Models. Rules for Obtaining Expecting Mean Squares. Nested Factors. Research Study: Factors Affecting Pressure Drops Across Expansion Joints . Summary. Exercises. 18. Split-Plot, Repeated Measures, and Crossover Designs. Introduction and Abstract of Research Study. Split-Plot Designs. Single-Factor Experiments with Repeated Measures on One of the Factors. Two-Factor Experiments with Repeated Measures on One of the Factors. Crossover Design. Research Study: Effects of Oil Spill on Plant Growth. Summary. Exercises. 19. Analysis of Variance for Some Unbalanced Designs. Introduction and Abstract of Research Study. A Randomized Block Design with One or More Missing Observations. A Latin Square Design with Missing Data. Balanced Incomplete Block (BIB) Designs. Research Study: Evaluation of the Consistency of Property Assessment. Summary and Key Formulas. Exercises. PART 7: COMMUNICATING AND DOCUMENTING THE RESULTS OF ANALYSES 20. Communicating and Documenting the Results of a Study or Experiment. Introduction. The Difficulty of Good Communication. Communication Hurdles: Graphical Distortions. Communication Hurdles: Biased Samples. Communication Hurdles: Sample Size. The Statistical Report. Documentation and Storage of Results. Summary. Exercises. © 2010, 1296pp, Paperback, 9780495109143 www.cengageasia.com 25 has been used to replace material that unnecessarily emphasized numerical calculations. • The complete set of data for most exercises is provided, along with related computer results. This allows the instructor to assign computer work based on available packaged programs. CONTENTS APPLIED REGRESSION ANALYSIS AND MULTIVARIABLE METHODS, INTERNATIONAL EDITION, 4E David G. Kleinbaum, Emory University; Lawrence L. Kupper, University of North Carolina at Chapel Hill; Azhar Nizam, Emory University; Keith E. Muller, University of North Carolina at Chapel Hill This bestseller is known for its modern, practical approach to regression-analysis methods that students will find applicable to real-life problems. APPLIED REGRESSION ANALYSIS AND MULTIVARIABLE METHODS highlights the role of the computer in contemporary statistics with numerous printouts and exercises that can be solved with the computer. The authors continue to emphasize model development, the intuitive logic and assumptions that underlie the techniques covered, the purposes, advantages, and disadvantages of the techniques, and valid interpretations of those techniques. NEW TO THIS EDITION • New chapters on Polytomous and Ordinal Logistic Regression (Chapter 23) and Sample Size Determination (Chapter 27). • Completely revised chapters on maximum likelihood (ML) estimation (Chapter 21), and analysis of correlated data (Chapter 25, 26). • Updated content, with new exercises added to several chapters. FEATURES • Numerous examples and exercises illustrate applications to real studies in a wide variety of disciplines. • Several advanced topics are presented in a unique, nonmathematical manner. • The quantity of computer results throughout the text has been greatly expanded. Computer output 1. CONCEPTS AND EXAMPLES OF RESEARCH. Concepts. Examples. Concluding Remarks. References. 2. CLASSIFICATION OF VARIABLES AND THE CHOICE OF ANALYSIS. Classification of Variables. Overlapping of Classification Schemes. Choice of Analysis. References. 3. BASIC STATISTICS: A REVIEW. Preview. Descriptive Statistics. Random Variables and Distributions. Sampling Distributions of t, ƒÓ2, and F. Statistical Inference: Estimation. Statistical Inference: Hypothesis Testing. Error Rate, Power, and Sample Size. Problems. References. 4. INTRODUCTION TO REGRESSION ANALYSIS. Preview. Association versus Causality. Statistical versus Deterministic Models. Concluding Remarks. References. 5. STRAIGHT-LINE REGRESSION ANALYSIS. Preview. Regression with a Single Independent Variable. Mathematical Properties of a Straight Line. Statistical Assumptions for a Straightline Model. Determining the Best-fitting Straight Line. Measure of the Quality of the Straight-line Fit and Estimate ƒã2. Inferences About the Slope and Intercept. Interpretations of Tests for Slope and Intercept. Inferences About the Regression Line ƒÝY|X = ƒÒ0 + ƒÒ1X . Prediction of a New Value of Y at X0. Problems. References. 6. THE CORRELATION COEFFICIENT AND STRAIGHT-LINE REGRESSION ANALYSIS. Definition of r. r as a Measure of Association. The Bivariate Normal Distribution. r and the Strength of the Straight-line Relationship. What r Does Not Measure. Tests of Hypotheses and Confidence Intervals for the Correlation Coefficient. Testing for the Equality of Two Correlations. Problems. References. 7. THE ANALYSIS-OF-VARIANCE TABLE. Preview. The ANOVA Table for Straight-line Regression. Problems. 8. MULTIPLE REGRESSION ANALYSIS: GENERAL CONSIDERATIONS. Preview. Multiple Regression Models. Graphical Look at the Problem. Assumptions of Multiple Regression. Determining the Best Estimate of the Multiple Regression Equation. The ANOVA Table for Multiple Regression. Numerical Examples. Problems. References. 9. TESTING HYPOTHESES IN MULTIPLE REGRESSION. www.cengageasia.com 26 Preview. Test for Significant Overall Regression. Partial F Test. Multiple Partial F Test. Strategies for Using Partial F Tests. Tests Involving the Intercept. Problems. References. 10. CORRELATIONS: MULTIPLE, PARTIAL, AND MULTIPLE PARTIAL. Preview. Correlation Matrix. Multiple Correlation Coefficient. Relationship of RY|X1, X2, ¡KXk to the Multivariate Normal Distribution. Partial Correlation Coefficient. Alternative Representation of the Regression Model. Multiple Partial Correlation. Concluding Remarks. Problems. References. 11. CONFOUNDING AND INTERACTION IN REGRESSION. Preview. Overview. Interaction in Regression. Confounding in Regression. Summary and Conclusions. Problems. References. 12. DUMMY VARIABLES IN REGRESSION. Preview. Definitions. Rule for Defining Dummy Variables. Comparing Two Straight-line Regression Equations: An Example. Questions for Comparing Two Straight Lines. Methods of Comparing Two Straight Lines. Method I: Using Separate Regression Fits to Compare Two Straight Lines. Method II: Using a Single Regression Equation to Compare Two Straight Lines. Comparison of Methods I and II. Testing Strategies and Interpretation: Comparing Two Straight Lines. Other Dummy Variable Models. Comparing Four Regression Equations. Comparing Several Regression Equations Involving Two Nominal Variables. Problems. References. 13. ANALYSIS OF COVARIANCE AND OTHER METHODS FOR ADJUSTING CONTINUOUS DATA. Preview. Adjustment Problem. Analysis of Covariance. Assumption of Parallelism: A Potential Drawback. Analysis of Covariance: Several Groups and Several Covariates. Comments and Cautions. Summary Problems. Reference. 14. REGRESSION DIAGNOSTICS. Preview. Simple Approaches to Diagnosing Problems in Data. Residual Analysis: Detecting Outliers and Violations of Model Assumptions. Strategies of Analysis. Collinearity. Scaling Problems. Diagnostics Example. An Important Caution. Problems. References. 15. POLYNOMIAL REGRESSION. Preview. Polynomial Models. Least-squares Procedure for Fitting a Parabola. ANOVA Table for Second-order Polynomial Regression. Inferences Associated with Second-order Polynomial Regression. Example Requiring a Second-order Model. Fitting and Testing Higher-order Model. Lack-of-fit Tests. Orthogonal Polynomials. Strategies for Choosing a Polynomial Model. Problems. 16. SELECTING THE BEST REGRESSION EQUATION. Preview. Steps in Selecting the Best Regression Equation. Step 1: Specifying the Maximum Model. Step 2: Specifying a Criterion for Selecting a Model. Step 3: Specifying a Strategy for Selecting Variables. Step 4: Conducting the Analysis. Step 5: Evaluating Reliability with Split Samples. Example Analysis of Actual Data. Issues in Selecting the Most Valid Model. Problems. References. 17. ONE-WAY ANALYSIS OF VARIANCE. Preview. One-way ANOVA: The Problem, Assumptions, and Data Configuration. for One-way Fixed-effects ANOVA. Regression Model for Fixed-effects One-way ANOVA Fixed-effects Model for One-way ANOVA. Random-effects Model for One-way ANOVA. -comparison Procedures for Fixed-effects Oneway ANOVA. a Multiple-comparison Technique. Orthogonal Contrasts and Partitioning an ANOVA Sum of Squares. Problems. References. 18. RANDOMIZED BLOCKS: SPECIAL CASE OF TWO-WAY ANOVA. Preview. Equivalent Analysis of a Matched-pairs Experiment. Principle of Blocking. Analysis of a Randomized-blocks Experiment. ANOVA Table for a Randomized-blocks Experiment. Models for a Randomized-blocks Experiment. Fixed-effects ANOVA Model for a Randomized-blocks Experiment. Problems. References. 19. TWO-WAY ANOVA WITH EQUAL CELL NUMBERS. Preview. Using a Table of Cell Means. General Methodology. F Tests for Two-way ANOVA. Regression Model for Fixed-effects Two-way ANOVA. Interactions in Two-way ANOVA. Random- and Mixedeffects Two-way ANOVA Models. Problems. References. 20. TWO-WAY ANOVA WITH UNEQUAL CELL NUMBERS. Preview. Problem with Unequal Cell Numbers: Nonorthogonality. Regression Approach for Unequal Cell Sample Sizes. Higher-way ANOVA. Problems. References. 21. THE METHOD OF MAXIMUM LIKELIHOOD. Preview. The Principle of Maximum Likelihood. Statistical Inference Using Maximum Likelihood. Summary. Problems. 22. LOGISTIC REGRESSION ANALYSIS. Preview. The Logistic Model. Estimating the Odds Ratio Using Logistic Regression. A Numerical Example of Logistic Regression. Theoretical Considerations. An Example of Conditional ML Estimation Involving Pair-matched Data with Unmatched Covariates. Summary. Problems. References. 23. POLYTOMOUS AND ORDINAL LOGISTIC REGRESSION. Preview. Why Not Use Binary Regression? An Example of Polytomous Logistic Regression: One Predictor, Three Outcome Categories. An Example: Extending the www.cengageasia.com 27 Polytomous Logistic Model to Several Predictors. Ordinal Logistic Regression: Overview. A “Simple” Hypothetical Example: Three Ordinal Categories and One Dichotomous Exposure Variable. Ordinal Logistic Regression Example Using Real Data with Four Ordinal Categories and Three Predictor Variables. Summary. Problems. References. 24. POISSON REGRESSION ANALYSIS. Preview. The Poisson Distribution. Example of Poisson Regression. Poisson Regression: General Considerations. Measures of Goodness of Fit. Continuation of Skin Cancer Data Example. A Second Illustration of Poisson Regression Analysis. Summary. Problems. References. 25. ANALYSIS OF CORRELATED DATA PART 1: THE GENERAL LINEAR MIXED MODEL. Preview. Examples. General Linear Mixed Model Approach. Example: Study of Effects of an Air Polluion Episode on FEV1 Levels. Summary¡XAnalysis of Correlated Data: Part 1. Problems. References. 26. ANALYSIS OF CORRELATED DATA PART 2: RANDOM EFFECTS AND OTHER ISSUES. Preview. Random Effects Revisited. Results for Random Effects Models Applied to Air Pollution Study Data. Second Example¡XAnalysis of Posture Measurement Data. Recommendations about Choice of Correlation Structure. Analysis of Data for Discrete Outcomes. Problems. References. 27. SAMPLE SIZE PLANNING FOR LINEAR AND LOGISTIC REGRESSION AND ANALYSIS OF VARIANCE. Preview. Review: Sample Size Calculations for Comparisons of Means and Proportions. Sample Size Planning for Linear Regression. Sample Size Planning for Logistic Regression. Power and Sample Size Determination for Linear Models: A General Approach. Sample Size Determination for Matched Case-control Studies with a Dichotomous Outcome. Practical Considerations and Cautions. Problems. References. Appendix A. Appendix B. Appendix C. Solutions to Exercises. Index. © 2008, 940pp, Paperback, 9780495384984 APPLIED REGRESSION ANALYSIS AND OTHER MULTIVARIABLE METHODS, 4E David G. Kleinbaum, Emory University; Lawrence L. Kupper, University of North Carolina at Chapel Hill; Azhar Nizam, Emory University; Keith E. Muller, University of North Carolina at Chapel Hill This bestseller is known for its modern, practical approach to regression-analysis methods that students will find applicable to real-life problems. APPLIED REGRESSION ANALYSIS AND MULTIVARIABLE METHODS highlights the role of the computer in contemporary statistics with numerous printouts and exercises that can be solved with the computer. The authors continue to emphasize model development, the intuitive logic and assumptions that underlie the techniques covered, the purposes, advantages, and disadvantages of the techniques, and valid interpretations of those techniques. NEW TO THIS EDITION • New chapters on Polytomous and Ordinal Logistic Regression (Chapter 23) and Sample Size Determination (Chapter 27). • Completely revised chapters on maximum likelihood (ML) estimation (Chapter 21), and analysis of correlated data (Chapter 25, 26). • Updated content, with new exercises added to several chapters. FEATURES • Numerous examples and exercises illustrate applications to real studies in a wide variety of disciplines. • Several advanced topics are presented in a unique, nonmathematical manner. • The quantity of computer results throughout the text has been greatly expanded. Computer output has been used to replace material that unnecessarily www.cengageasia.com 28 emphasized numerical calculations. • The complete set of data for most exercises is provided, along with related computer results. This allows the instructor to assign computer work based on available packaged programs. CONTENTS 1. Concepts And Examples Of Research. 2. Classification Of Variables And The Choice Of Analysis. 3. Basic Statistics: A Review. 4. Introduction To Regression Analysis. 5. Straight-Line Regression Analysis. 6. The Correlation Coefficient And Straight-Line Regression Analysis. 7. The Analysis-Of-Variance Table. 8. Multiple Regression Analysis: General Considerations. 9. Testing Hypotheses In Multiple Regression. 10. Correlations: Multiple, Partial, And Multiple Partial. 11. Confounding And Interaction In Regression. 12. Dummy Variables In Regression. 13. Analysis Of Covariance And Other Methods For Adjusting Continuous Data. 14. Regression Diagnostics. 15. Polynomial Regression. 16. Selecting The Best Regression Equation. 17. One-Way Analysis Of Variance. 18. Randomized Blocks: Special Case Of Two-Way Anova. 19. Two-Way Anova With Equal Cell Numbers. 20. Two-Way Anova With Unequal Cell Numbers. 21. The Method Of Maximum Likelihood. 22. Logistic Regression Analysis. 23. Polytomous And Ordinal Logistic Regression. 24. Poisson Regression Analysis. 27. Sample Size Planning For Linear And Logistic Regression And Analysis Of Variance. © 2008, 928pp, Hardback, 9780495384960 APPLIED REGRESSION ANALYSIS AND OTHER MULTIVARIABLE METHODS, 5E David G. Kleinbaum, Emory University; Lawrence L. Kupper, University of North Carolina at Chapel Hill; Azhar Nizam, Emory University; Eli S. Rosenberg, Emory University This bestseller is known for its modern, practical approach to regression-analysis methods that students will find applicable to real-life problems. APPLIED REGRESSION ANALYSIS AND MULTIVARIABLE METHODS highlights the role of the computer in contemporary statistics with numerous printouts and exercises that can be solved with the computer. The authors continue to emphasize model development, the intuitive logic and assumptions that underlie the techniques covered, the purposes, advantages, and disadvantages of the techniques, and valid interpretations of those techniques. Available with InfoTrac® Student Collections http://gocengage.com/ infotrac. NEW TO THIS EDITION • New Linear Regression Examples throughout the text. • New SAS Computer Appendix. • New and Revised Problems in selected chapters. • Revised, clarified exposition and a Revised Sample Size Chapter. • Improved Design for maximum student engagement. FEATURES • Numerous examples and exercises illustrate applications to real studies in a wide variety of disciplines. • Chapters on Polytomous and Ordinal Logistic Regression (Chapter 23) and Sample Size Determination (Chapter 27). • Chapters on maximum likelihood (ML) estimation (Chapter 21), and analysis of correlated data (Chapter www.cengageasia.com 29 25, 26). • Updated content, with new exercises added to several chapters. • Numerous examples and exercises illustrate applications to real studies in a wide variety of disciplines. CONTENTS 1. CONCEPTS AND EXAMPLES OF RESEARCH. Concepts. Examples. Concluding Remarks. References. 2. CLASSIFICATION OF VARIABLES AND THE CHOICE OF ANALYSIS. Classification of Variables. Overlapping of Classification Schemes. Choice of Analysis. References. 3. BASIC STATISTICS: A REVIEW. Preview. Descriptive Statistics. Random Variables and Distributions. Sampling Distributions of t, Ó2, and F. Statistical Inference: Estimation. Statistical Inference: Hypothesis Testing. Error Rate, Power, and Sample Size. Problems. References. 4. INTRODUCTION TO REGRESSION ANALYSIS. Preview. Association versus Causality. Statistical versus Deterministic Models. Concluding Remarks. References. 5. STRAIGHT-LINE REGRESSION ANALYSIS. Preview. Regression with a Single Independent Variable. Mathematical Properties of a Straight Line. Statistical Assumptions for a Straightline Model. Determining the Best-fitting Straight Line. Measure of the Quality of the Straight-line Fit and Estimate ã2. Inferences About the Slope and Intercept. Interpretations of Tests for Slope and Intercept. Inferences About the Regression Line ÝY|X = Ò0 + Ò1X . Prediction of a New Value of Y at X0. Problems. References. 6. THE CORRELATION COEFFICIENT AND STRAIGHT-LINE REGRESSION ANALYSIS. Definition of r. r as a Measure of Association. The Bivariate Normal Distribution. r and the Strength of the Straight-line Relationship. What r Does Not Measure. Tests of Hypotheses and Confidence Intervals for the Correlation Coefficient. Testing for the Equality of Two Correlations. Problems. References. 7. THE ANALYSIS-OF-VARIANCE TABLE. Preview. The ANOVA Table for Straight-line Regression. Problems. 8. MULTIPLE REGRESSION ANALYSIS: GENERAL CONSIDERATIONS. Preview. Multiple Regression Models. Graphical Look at the Problem. Assumptions of Multiple Regression. Determining the Best Estimate of the Multiple Regression Equation. The ANOVA Table for Multiple Regression. Numerical Examples. Problems. References. 9. TESTING HYPOTHESES IN MULTIPLE REGRESSION. Preview. Test for Significant Overall Regression. Partial F Test. Multiple Partial F Test. Strategies for Using Partial F Tests. Tests Involving the Intercept. Problems. References. 10. CORRELATIONS: MULTIPLE, PARTIAL, AND MULTIPLE PARTIAL. Preview. Correlation Matrix. Multiple Correlation Coefficient. Relationship of RY|X1, X2, ¡KXk to the Multivariate Normal Distribution. Partial Correlation Coefficient. Alternative Representation of the Regression Model. Multiple Partial Correlation. Concluding Remarks. Problems. References. 11. CONFOUNDING AND INTERACTION IN REGRESSION. Preview. Overview. Interaction in Regression. Confounding in Regression. Summary and Conclusions. Problems. References. 12. DUMMY VARIABLES IN REGRESSION. Preview. Definitions. Rule for Defining Dummy Variables. Comparing Two Straight-line Regression Equations: An Example. Questions for Comparing Two Straight Lines. Methods of Comparing Two Straight Lines. Method I: Using Separate Regression Fits to Compare Two Straight Lines. Method II: Using a Single Regression Equation to Compare Two Straight Lines. Comparison of Methods I and II. Testing Strategies and Interpretation: Comparing Two Straight Lines. Other Dummy Variable Models. Comparing Four Regression Equations. Comparing Several Regression Equations Involving Two Nominal Variables. Problems. References. 13. ANALYSIS OF COVARIANCE AND OTHER METHODS FOR ADJUSTING CONTINUOUS DATA. Preview. Adjustment Problem. Analysis of Covariance. Assumption of Parallelism: A Potential Drawback. Analysis of Covariance: Several Groups and Several Covariates. Comments and Cautions. Summary Problems. Reference. 14. REGRESSION DIAGNOSTICS. Preview. Simple Approaches to Diagnosing Problems in Data. Residual Analysis: Detecting Outliers and Violations of Model Assumptions. Strategies of Analysis. Collinearity. Scaling Problems. Diagnostics Example. An Important Caution. Problems. References. 15. POLYNOMIAL REGRESSION. Preview. Polynomial Models. Least-squares Procedure for Fitting a Parabola. ANOVA Table for Second-order Polynomial Regression. Inferences Associated with Second-order Polynomial Regression. Example Requiring a Second-order Model. Fitting and Testing Higher-order Model. Lack-of-fit Tests. Orthogonal Polynomials. Strategies for Choosing a Polynomial Model. Problems. 16. SELECTING THE BEST REGRESSION EQUATION. Preview. Steps in Selecting www.cengageasia.com 30 the Best Regression Equation. Step 1: Specifying the Maximum Model. Step 2: Specifying a Criterion for Selecting a Model. Step 3: Specifying a Strategy for Selecting Variables. Step 4: Conducting the Analysis. Step 5: Evaluating Reliability with Split Samples. Example Analysis of Actual Data. Issues in Selecting the Most Valid Model. Problems. References. 17. ONE-WAY ANALYSIS OF VARIANCE. Preview. One-way ANOVA: The Problem, Assumptions, and Data Configuration. for One-way Fixed-effects ANOVA. Regression Model for Fixed-effects One-way ANOVA Fixed-effects Model for One-way ANOVA. Random-effects Model for One-way ANOVA. -comparison Procedures for Fixed-effects Oneway ANOVA. a Multiple-comparison Technique. Orthogonal Contrasts and Partitioning an ANOVA Sum of Squares. Problems. References. 18. RANDOMIZED BLOCKS: SPECIAL CASE OF TWO-WAY ANOVA. Preview. Equivalent Analysis of a Matched-pairs Experiment. Principle of Blocking. Analysis of a Randomized-blocks Experiment. ANOVA Table for a Randomized-blocks Experiment. Models for a Randomized-blocks Experiment. Fixed-effects ANOVA Model for a Randomized-blocks Experiment. Problems. References. 19. TWO-WAY ANOVA WITH EQUAL CELL NUMBERS. Preview. Using a Table of Cell Means. General Methodology. F Tests for Two-way ANOVA. Regression Model for Fixed-effects Two-way ANOVA. Interactions in Two-way ANOVA. Random- and Mixedeffects Two-way ANOVA Models. Problems. References. 20. TWO-WAY ANOVA WITH UNEQUAL CELL NUMBERS. Preview. Problem with Unequal Cell Numbers: Nonorthogonality. Regression Approach for Unequal Cell Sample Sizes. Higher-way ANOVA. Problems. References. 21. THE METHOD OF MAXIMUM LIKELIHOOD. Preview. The Principle of Maximum Likelihood. Statistical Inference Using Maximum Likelihood. Summary. Problems. 22. LOGISTIC REGRESSION ANALYSIS. Preview. The Logistic Model. Estimating the Odds Ratio Using Logistic Regression. A Numerical Example of Logistic Regression. Theoretical Considerations. An Example of Conditional ML Estimation Involving Pair-matched Data with Unmatched Covariates. Summary. Problems. References. 23. POLYTOMOUS AND ORDINAL LOGISTIC REGRESSION. Preview. Why Not Use Binary Regression? An Example of Polytomous Logistic Regression: One Predictor, Three Outcome Categories. An Example: Extending the Polytomous Logistic Model to Several Predictors. Ordinal Logistic Regression: Overview. A “Simple” Hypothetical Example: Three Ordinal Categories and One Dichotomous Exposure Variable. Ordinal Logistic Regression Example Using Real Data with Four Ordinal Categories and Three Predictor Variables. Summary. Problems. References. 24. POISSON REGRESSION ANALYSIS. Preview. The Poisson Distribution. Example of Poisson Regression. Poisson Regression: General Considerations. Measures of Goodness of Fit. Continuation of Skin Cancer Data Example. A Second Illustration of Poisson Regression Analysis. Summary. Problems. References. 25. ANALYSIS OF CORRELATED DATA PART 1: THE GENERAL LINEAR MIXED MODEL. Preview. Examples. General Linear Mixed Model Approach. Example: Study of Effects of an Air Polluion Episode on FEV1 Levels. Summary¡XAnalysis of Correlated Data: Part 1. Problems. References. 26. ANALYSIS OF CORRELATED DATA PART 2: RANDOM EFFECTS AND OTHER ISSUES. Preview. Random Effects Revisited. Results for Random Effects Models Applied to Air Pollution Study Data. Second Example¡XAnalysis of Posture Measurement Data. Recommendations about Choice of Correlation Structure. Analysis of Data for Discrete Outcomes. Problems. References. 27. SAMPLE SIZE PLANNING FOR LINEAR AND LOGISTIC REGRESSION AND ANALYSIS OF VARIANCE. Preview. Review: Sample Size Calculations for Comparisons of Means and Proportions. Sample Size Planning for Linear Regression. Sample Size Planning for Logistic Regression. Power and Sample Size Determination for Linear Models: A General Approach. Sample Size Determination for Matched Case-control Studies with a Dichotomous Outcome. Practical Considerations and Cautions. Problems. References. Appendix A. Appendix B. Appendix C. Solutions to Exercises. Index. © 2014, 1072pp, Hardback, 9781285051086 www.cengageasia.com 31 friendly. FEATURES ELEMENTARY SURVEY SAMPLING, 7E Richard L. Scheaffer, University of Florida, Emeritus; William Mendenhall, III, University of Florida, Emeritus; R. Lyman Ott; Kenneth G. Gerow, University of Wyoming ELEMENTARY SURVEY SAMPLING introduces students to the design and analysis of sample surveys via a practical, engaging approach. First, this introductory text begins with brief chapters focused on the important role that sample surveys play in the modern world. Then, each successive chapter builds on this foundation. These chapters start with the problem, describe the methodology needed for solving the problem, and provide the details of the estimation procedure using a compact presentation of the necessary formulas. Each chapter then works out the practical example in full detail. Finally, at the end of each chapter, ELEMENTARY SURVEY SAMPLING includes a wealth of exercises that enable students to continue practicing and to stretch their grasp of the content. The text includes a complete package of interactive statistical tools for implementing all the calculations; text examples are built in to the tools. NEW TO THIS EDITION • Integrates new software: By using software that significantly decreases the need for long arithmetic, the authors have allowed students to focus less on arithmetic, and instead on the valid use of appropriate statistical tools. • New Icons: Excel Tools icons have been added to the boxed equations and example solutions, so as to direct students to use Excel when appropriate. • More concise data: The data for examples and exercises is now more concise and is also incorporated with the electronic supplements. • Updated Appendices: the Appendices for this edition have been updated to be more concise and user- • Bridges the gap between classroom and practice in two significant ways: First, select sections on weights in unequal probability sampling. Then, they expanded the treatment of nonresponse in Chapter 11, including the use of imputation as a technique for handling some types of nonresponse. • Emphasizes two cutting-edge sampling techniques: the bootstrap, a modern technique for establishing margins of error and confidence intervals in complex designs, and an adaptive sampling technique for improving estimates while the field work is in progress. • Incorporates compatible computations: ELEMENTARY SURVEY SAMPLING’s computations are compatible with modern statistical software, using much fewer hand calculation formulas. • Utilizes detailed simulations: Key statistical concepts are demonstrated using step-by-step simulations. • Emphasizes practical aspects: topics include sources of errors in surveys, methods of data collection, designing questionnaires, and guidelines for planning surveys. • Expands Student Understanding: The “Experiences with Real Data” sections at the end of most chapters enable students to work with both large and small projects, some requiring computations to be handled by a computer, giving them valuable experience. CONTENTS 1. INTRODUCTION. 2. ELEMENTS OF THE SAMPLING PROBLEM. Introduction. Technical Terms. How to Select the Sample: The Design of the Sample Survey. Sources of Errors in Surveys. Designing a Questionnaire. Planning a Survey. Summary. 3. SOME BASIC CONCEPTS OF STATISTICS. Introduction. Summarizing Information in Populations and Samples: The Infinite Population Case. Summarizing Information in Populations and Samples: The Finite Population Case. Sampling Distributions. Covariance and Correlation. Estimation. Summary. 4. SIMPLE RANDOM SAMPLING. Introduction. How to Draw a Simple Random Sample. Estimation of a Population Mean and Total. Selecting the Sample Size for Estimating Population Means and Totals. Estimation of a Population Proportion. Comparing Estimates. Summary. 5. STRATIFIED RANDOM SAMPLING. www.cengageasia.com 32 Introduction. How to Draw a Stratified Random Sample. Estimation of a Population Mean and Total. Selecting the Sample Size for Estimating Population Means and Totals. Allocation of the Sample. Estimation of a Population Proportion. Selecting the Sample Size and Allocating the Sample to Estimate Proportions. Additional Comments on Stratified Sampling. An Optimal Rule for Choosing Strata. Stratification after Selection of the Sample. Double Sampling for Stratification. Summary. 6. RATIO, REGRESSION, AND DIFFERENCE ESTIMATION. Introduction. Surveys that Require the Use of Ratio Estimators. Ratio Estimation Using Simple Random Sampling. Selecting the Sample Size. Ratio Estimation in Stratified Random Sampling. Regression Estimation. Difference Estimation. Relative Efficiency of Estimators. Summary. 7. SYSTEMATIC SAMPLING. Introduction. How to Draw a Systematic Sample. Estimation of a Population Mean and Total. Estimation of a Population Proportion. Selecting the Sample Size. Repeated Systematic Sampling. Further Discussion of Variance Estimators. Summary. 8. CLUSTER SAMPLING. Introduction. How to Draw a Cluster Sample. Estimation of a Population Mean and Total. Equal Cluster Sizes; Comparison to Simple Random Sampling. Selecting the Sample Size for Estimating Population Means and Totals. Estimation of a Population Proportion. Selecting the Sample Size for Estimating Proportions. Cluster Sampling Combined with Stratification. Cluster Sampling with Probabilities Proportional to Size. Summary. 9. TWOSTAGE CLUSTER SAMPLING. Introduction. How to Draw a Two-Stage Cluster Sample. Unbiased Estimation of a Population Mean and Total. Ratio Estimation of a Population Mean. Estimation of a Population Proportion. Sampling Equal-Sized Clusters. Two-Stage Cluster Sampling with Probabilities Proportional to Size. Summary. 10. ESTIMATING THE POPULATION SIZE. Introduction. Estimation of a Population Size Using Direct Sampling. Estimation of a Population Size Using Inverse Sampling. Choosing Sample Sizes for Direct and Inverse Sampling. Estimating Population Density and Size from Quadrat Samples. Estimating Population Density and Size from Stocked Quadrats. Adaptive Sampling. Summary. 11. SUPPLEMENTAL TOPICS. Introduction. Interpenetrating Subsamples. Estimation of Means and Totals over Subpopulations. Random-Response Model. Use of Weights in Sample Surveys. Adjusting for Nonresponse. Imputation. Selecting the Number of Callbacks. The Bootstrap. Summary. 12. SUMMARY. Summary of the Designs and Methods. Comparisons among the Designs and Methods. Appenidices. References and Bibliography Tables. Derivation of Some Main Results. Macros for MINITAB. Macros for SAS. Data Sets. Selected Answers. Index. © 2012, 480pp, Hardback, 9780840053619 INTRODUCTION TO PROBABILITY AND ITS APPLICATIONS, 3E Richard L. Scheaffer, University of Florida, Emeritus; Linda Young, University of Florida, Gainesville This text focuses on the utility of probability in solving real-world problems for students in a one-semester calculus-based probability course. Theory is developed to a practical degree and grounded in discussion of its practical uses in solving real-world problems. Numerous applications using up-to-date real data in engineering and the life, social, and physical sciences illustrate and motivate the many ways probability affects our lives. The text’s accessible presentation carefully progresses from routine to more difficult problems to suit students of different backgrounds, and carefully explains how and where to apply methods. Students going on to more advanced courses in probability and statistics will gain a solid background in fundamental concepts and theory, while students who must apply probability to their courses engineering and the sciences will develop a working knowledge of the subject and appreciation of its practical power. NEW TO THIS EDITION • Greater variety of applications--Many applied examples and problems, especially from the biological www.cengageasia.com 33 sciences, have been added to the existing array from the social and engineering sciences. • Updated exercises using real data--Problems, often based on surveys from the U.S. Census or some other national survey, are updated with the most current data available. • Expanded discussions of key topics--Among other changes, this edition includes more guidance on methods and when to use specific methods, a fuller discussion of sets as they relate to probability, and explanation of discrete multivariate distributions and transformations for discrete distributions. • Applets for visualizing concepts--Referenced in the text and available on the Book Companion Website, applets provide an optional means for students’ active exploration by illustrating basic concepts while allowing for easy computations. FEATURES • Emphasis on practical aspects of probabilistic interpretation of data--Real-life applications from a variety of fields motivate the study of probability by illustrating the many ways it affects students’ lives. • Theory presented in context--Theory is developed to a practical degree around models used in real-world applications, while proofs of theorems and “tricky” probability calculations are minimized. • Variety of problems--Problem sets carefully progress from routine to more difficult to suit students from varying academic backgrounds. CONTENTS 1. PROBABILITY IN THE WORLD AROUND US. Why Study Probability? Deterministic and Probabilistic Models. Modeling Reality. Deterministic Models. Probabilistic Models. Applications in Probability. A Brief Historical Note. A Look Ahead. 2. FOUNDATIONS OF PROBABILITY. Understanding Randomness: An Intuitive Notion of Probability. Randomness with Known Structure. Randomness with Unknown Structure. Sampling a Finite Universe. Sample Space and Events. Definition of Probability. Counting Rules Useful in Probability. More Counting Rules Useful in Probability. Summary. 3. CONDITIONAL PROBABILITY AND INDEPENDENCE. Conditional Probability. Independence. Theorem of Total Probability and Bayes’ Rule. Odds, Odds Ratios, and Relative Risk. Summary. 4. DISCRETE PROBABILITY DISTRIBUTIONS. Random Variables and Their Probability Distributions. Expected Values of Random Variables. The Bernoulli Distribution. The Binomial Distribution. Probability Function. Mean and Variance. History and Applications. The Geometric Distribution. Probability Function. Mean and Variance. An Alternate Parameterization: Number of Trials Versus Number of Failures. The Negative Binomial Distribution. Probability Function. Mean and Variance. An Alternate Parameterization: Number of Trials Versus Number of Failures. History and Applications. The Poisson Distribution. Probability Function. Mean and Variance. History and Applications. The Hypergeometric Distribution. The Probability Function. Mean and Variance. History and Applications. The Momentgenerating Function. The Probability-generating Function. Markov Chains. Summary. 5. CONTINUOUS PROBABILITY DISTRIBUTIONS. Continuous Random Variables and Their Probability Distributions. Expected Values of Continuous Random Variables. The Uniform Distribution. Probability Density Function. Mean and Variance. History and Applications. The Exponential Distribution. Probability Density Function. Mean and Variance. Properties. History and Applications. The Gamma Distribution. Probability Density Function. Mean and Variance. History and Applications. The Normal Distribution. The Normal Probability Density Function. Mean and Variance. Calculating Normal Probabilities. Applications to Real Data. Quantile-Quantile (Q-Q) Plots. History. The Beta Distribution. Probability Density Function. Mean and Variance. H istory and Applications. The Weibull Distribution. Probability Density Function. Mean and Variance. History and Applications to Real Data. Reliability. Hazard Rate Function. Series and Parallel Systems. Redundancy. Moment-generating Functions for Continuous Random Variables. Expectations of Discontinuous Functions and Mixed Probability Distributions. Summary. 6. MULTIVARIATE PROBABILITY DISTRIBUTIONS. Bivariate and Marginal Probability Distributions. Conditional Probability Distributions. Independent Random Variables. Expected Values of Functions of Random Variables. Conditional Expectations. The Multinomial Distribution. More on the Moment-Generating Function. Compounding and Its Applications. Summary. 7. FUNCTIONS OF RANDOM VARIABLES. Introduction. Functions of Discrete Random Variables. Method of Distribution Functions. Method of Transformations in One Dimension. Method www.cengageasia.com 34 of Conditioning. Method of Moment-Generating Functions. Gamma Case. Normal Case. Normal and Gamma Relationships. Method of Transformation—Two Dimensions. Order Statistics. Probability-Generating Functions: Applications to Random Sums of Random Variables. Summary. 8. SOME APPROXIMATIONS TO PROBABILITY DISTRIBUTIONS: LIMIT THEOREMS. Introduction. Convergence in Probability. Convergence in Distribution. The Central Limit Theorem. Combination of Convergence in Probability and Convergence in Distribution. Summary. 9. EXTENSIONS OF PROBABILITY THEORY. The Poisson Process. Birth and Death Processes: Biological Applications. Queues: Engineering Applications. Arrival Times for the Poisson Process. Infinite Server Queue. Renewal Theory: Reliability Applications. Summary. Appendix Tables. © 2010, 480pp, Hardback, 9780534386719 gain a solid background in fundamental concepts and theory, while students who must apply probability to their courses engineering and the sciences will develop a working knowledge of the subject and appreciation of its practical power. NEW TO THIS EDITION • Greater variety of applications—Many applied examples and problems, especially from the biological sciences, have been added to the existing array from the social and engineering sciences. • Updated exercises using real data—Problems, often based on surveys from the U.S. Census or some other national survey, are updated with the most current data available. • Expanded discussions of key topics—Among other changes, this edition includes more guidance on methods and when to use specific methods, a fuller discussion of sets as they relate to probability, and explanation of discrete multivariate distributions and transformations for discrete distributions. • Applets for visualizing concepts—Referenced in the text and available on the Book Companion Website, applets provide an optional means for students’ active exploration by illustrating basic concepts while allowing for easy computations. FEATURES INTRODUCTION TO PROBABILITY AND ITS APPLICATIONS, INTERNATIONAL EDITION, 3E Richard L. Scheaffer, University of Florida, Emeritus; Linda Young, University of Florida, Gainesville This text focuses on the utility of probability in solving real-world problems for students in a one-semester calculus-based probability course. Theory is developed to a practical degree and grounded in discussion of its practical uses in solving real-world problems. Numerous applications using up-to-date real data in engineering and the life, social, and physical sciences illustrate and motivate the many ways probability affects our lives. The text’s accessible presentation carefully progresses from routine to more difficult problems to suit students of different backgrounds, and carefully explains how and where to apply methods. Students going on to more advanced courses in probability and statistics will • Emphasis on practical aspects of probabilistic interpretation of data—Real-life applications from a variety of fields motivate the study of probability by illustrating the many ways it affects students’ lives. • Theory presented in context—Theory is developed to a practical degree around models used in real-world applications, while proofs of theorems and “tricky” probability calculations are minimized. • Variety of problems—Problem sets carefully progress from routine to more difficult to suit students from varying academic backgrounds. CONTENTS 1. PROBABILITY IN THE WORLD AROUND US. Why Study Probability? Deterministic and Probabilistic Models. Modeling Reality. Deterministic Models. Probabilistic Models. Applications in Probability. A Brief Historical Note. A Look Ahead. 2. FOUNDATIONS OF PROBABILITY. Understanding Randomness: An Intuitive Notion of Probability. Randomness www.cengageasia.com 35 with Known Structure. Randomness with Unknown Structure. Sampling a Finite Universe. Sample Space and Events. Definition of Probability. Counting Rules Useful in Probability. More Counting Rules Useful in Probability. Summary. 3. CONDITIONAL PROBABILITY AND INDEPENDENCE. Conditional Probability. Independence. Theorem of Total Probability and Bayes’ Rule. Odds, Odds Ratios, and Relative Risk. Summary. 4. DISCRETE PROBABILITY DISTRIBUTIONS. Random Variables and Their Probability Distributions. Expected Values of Random Variables. The Bernoulli Distribution. The Binomial Distribution. Probability Function. Mean and Variance. History and Applications. The Geometric Distribution. Probability Function. Mean and Variance. An Alternate Parameterization: Number of Trials Versus Number of Failures. The Negative Binomial Distribution. Probability Function. Mean and Variance. An Alternate Parameterization: Number of Trials Versus Number of Failures. History and Applications. The Poisson Distribution. Probability Function. Mean and Variance. History and Applications. The Hypergeometric Distribution. The Probability Function. Mean and Variance. History and Applications. The Momentgenerating Function. The Probability-generating Function. Markov Chains. Summary. 5. CONTINUOUS PROBABILITY DISTRIBUTIONS. Continuous Random Variables and Their Probability Distributions. Expected Values of Continuous Random Variables. The Uniform Distribution. Probability Density Function. Mean and Variance. History and Applications. The Exponential Distribution. Probability Density Function. Mean and Variance. Properties. History and Applications. The Gamma Distribution. Probability Density Function. Mean and Variance. History and Applications. The Normal Distribution. The Normal Probability Density Function. Mean and Variance. Calculating Normal Probabilities. Applications to Real Data. Quantile-Quantile (Q-Q) Plots. History. The Beta Distribution. Probability Density Function. Mean and Variance. H istory and Applications. The Weibull Distribution. Probability Density Function. Mean and Variance. History and Applications to Real Data. Reliability. Hazard Rate Function. Series and Parallel Systems. Redundancy. Moment-generating Functions for Continuous Random Variables. Expectations of Discontinuous Functions and Mixed Probability Distributions. Summary. 6. MULTIVARIATE PROBABILITY DISTRIBUTIONS. Bivariate and Marginal Probability Distributions. Conditional Probability Distributions. Independent Random Variables. Expected Values of Functions of Random Variables. Conditional Expectations. The Multinomial Distribution. More on the Moment-Generating Function. Compounding and Its Applications. Summary. 7. FUNCTIONS OF RANDOM VARIABLES. Introduction. Functions of Discrete Random Variables. Method of Distribution Functions. Method of Transformations in One Dimension. Method of Conditioning. Method of Moment-Generating Functions. Gamma Case. Normal Case. Normal and Gamma Relationships. Method of Transformation—Two Dimensions. Order Statistics. Probability-Generating Functions: Applications to Random Sums of Random Variables. Summary. 8. SOME APPROXIMATIONS TO PROBABILITY DISTRIBUTIONS: LIMIT THEOREMS. Introduction. Convergence in Probability. Convergence in Distribution. The Central Limit Theorem. Combination of Convergence in Probability and Convergence in Distribution. Summary. 9. EXTENSIONS OF PROBABILITY THEORY. The Poisson Process. Birth and Death Processes: Biological Applications. Queues: Engineering Applications. Arrival Times for the Poisson Process. Infinite Server Queue. Renewal Theory: Reliability Applications. Summary. Appendix Tables. © 2010, 480pp, Paperback, 9781439047262 MATHEMATICAL STATISTICS WITH APPLICATIONS, 7E Dennis Wackerly, University of Florida; William Mendenhall, University of Florida, Emeritus; Richard L. Scheaffer, University of Florida, Emeritus In their bestselling MATHEMATICAL STATISTICS WITH APPLICATIONS, premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid foundation in statistical theory while conveying www.cengageasia.com 36 the relevance and importance of the theory in solving practical problems in the real world. The authors’ use of practical applications and excellent exercises helps students discover the nature of statistics and understand its essential role in scientific research. NEW TO THIS EDITION • A new chapter introducing Bayesian Methods for Inference (Chapter 16). • A collection of new applets allows students to further build their knowledge and understanding of probability and inference with interactive examples of key concepts. Many applets have associated exercises, allowing instructors to easily integrate the applets into students’ coursework. • Some of the applets permit determination of probabilities and quantiles associated with normal, gamma, beta, t, F and Chi-square distributions. • With other applets, students can assess the effect of different parameter choices on the shape of gamma and beta density functions. • Hundreds of new and updated exercises throughout the book complement the existing exercises. FEATURES • The authors efficiently use mathematics as a necessary tool to promote a firm understanding of statistical techniques. • Stressing connectivity, the authors explain not only how major topics play a role in statistical inference but also how the topics are related to one another. These integrating discussions appear most frequently in chapter introductions and conclusions. • This text takes a practical approach in both the exercises throughout and the useful topics in statistical methodology covered in the last five chapters. • Exercises are based on real data or actual experimental scenarios which allow students to see the practical uses of various statistical and probabilistic methods. CONTENTS 1. What Is Statistics? Introduction. Characterizing a Set of Measurements: Graphical Methods. Characterizing a Set of Measurements: Numerical Methods. How Inferences Are Made. Theory and Reality. Summary. 2. Probability. Introduction. Probability and Inference. A Review of Set Notation. A Probabilistic Model for an Experiment: The Discrete Case. Calculating the Probability of an Event: The Sample-Point Method. Tools for Counting Sample Points. Conditional Probability and the Independence of Events. Two Laws of Probability. Calculating the Probability of an Event: The Event-Composition Methods. The Law of Total Probability and Bayes’’’’s Rule. Numerical Events and Random Variables. Random Sampling. Summary. 3. Discrete Random Variables and Their Probability Distributions. Basic Definition. The Probability Distribution for Discrete Random Variable. The Expected Value of Random Variable or a Function of Random Variable. The Binomial Probability Distribution. The Geometric Probability Distribution. The Negative Binomial Probability Distribution (Optional). The Hypergeometric Probability Distribution. Moments and Moment-Generating Functions. Probability-Generating Functions (Optional). Tchebysheff’’’’s Theorem. Summary. 4. Continuous Random Variables and Their Probability Distributions. Introduction. The Probability Distribution for Continuous Random Variable. The Expected Value for Continuous Random Variable. The Uniform Probability Distribution. The Normal Probability Distribution. The Gamma Probability Distribution. The Beta Probability Distribution. Some General Comments. Other Expected Values. Tchebysheff’’’’s Theorem. Expectations of Discontinuous Functions and Mixed Probability Distributions (Optional). Summary. 5. Multivariate Probability Distributions. Introduction. Bivariate and Multivariate Probability Distributions. Independent Random Variables. The Expected Value of a Function of Random Variables. Special Theorems. The Covariance of Two Random Variables. The Expected Value and Variance of Linear Functions of Random Variables. The Multinomial Probability Distribution. The Bivariate Normal Distribution (Optional). Conditional Expectations. Summary. 6. Functions of Random Variables. Introductions. Finding the Probability Distribution of a Function of Random Variables. The Method of Distribution Functions. The Methods of Transformations. Multivariable Transformations Using Jacobians. Order Statistics. Summary. 7. Sampling Distributions and the Central Limit Theorem. Introduction. Sampling Distributions Related to the Normal Distribution. The Central Limit Theorem. A Proof of the Central Limit Theorem (Optional). The Normal Approximation to the Binomial Distributions. Summary. 8. Estimation. Introduction. The Bias and www.cengageasia.com 37 Mean Square Error of Point Estimators. Some Common Unbiased Point Estimators. Evaluating the Goodness of Point Estimator. Confidence Intervals. Large-Sample Confidence Intervals Selecting the Sample Size. SmallSample Confidence Intervals for u and u1-u2. Confidence Intervals for o2. Summary. 9. Properties of Point Estimators and Methods of Estimation. Introduction. Relative Efficiency. Consistency. Sufficiency. The Rao-Blackwell Theorem and Minimum-Variance Unbiased Estimation. The Method of Moments. The Method of Maximum Likelihood. Some Large-Sample Properties of MLEs (Optional). Summary. 10. Hypothesis Testing. Introduction. Elements of a Statistical Test. Common Large-Sample Tests. Calculating Type II Error Probabilities and Finding the Sample Size for the Z Test. Relationships Between Hypothesis Testing Procedures and Confidence Intervals. Another Way to Report the Results of a Statistical Test: Attained Significance Levels or p-Values. Some Comments on the Theory of Hypothesis Testing. Small-Sample Hypothesis Testing for u and u1-u2. Testing Hypotheses Concerning Variances. Power of Test and the NeymanPearson Lemma. Likelihood Ration Test. Summary. 11. Linear Models and Estimation by Least Squares. Introduction. Linear Statistical Models. The Method of Least Squares. Properties of the Least Squares Estimators for the Simple Linear Regression Model. Inference Concerning the Parameters BI. Inferences Concerning Linear Functions of the Model Parameters: Simple Linear Regression. Predicting a Particular Value of Y Using Simple Linear Regression. Correlation. Some Practical Examples. Fitting the Linear Model by Using Matrices. Properties of the Least Squares Estimators for the Multiple Linear Regression Model. Inferences Concerning Linear Functions of the Model Parameters: Multiple Linear Regression. Prediction a Particular Value of Y Using Multiple Regression. A Test for H0: Bg+1 + Bg+2 = . = Bk = 0. Summary and Concluding Remarks. 12. Considerations in Designing Experiments. The Elements Affecting the Information in a Sample. Designing Experiment to Increase Accuracy. The Matched Pairs Experiment. Some Elementary Experimental Designs. Summary. 13. The Analysis of Variance. Introduction. The Analysis of Variance Procedure. Comparison of More than Two Means: Analysis of Variance for a One-way Layout. An Analysis of Variance Table for a One-Way Layout. A Statistical Model of the One-Way Layout. Proof of Additivity of the Sums of Squares and E (MST) for a One-Way Layout (Optional). Estimation in the OneWay Layout. A Statistical Model for the Randomized Block Design. The Analysis of Variance for a Randomized Block Design. Estimation in the Randomized Block Design. Selecting the Sample Size. Simultaneous Confidence Intervals for More than One Parameter. Analysis of Variance Using Linear Models. Summary. 14. Analysis of Categorical Data. A Description of the Experiment. The Chi-Square Test. A Test of Hypothesis Concerning Specified Cell Probabilities: A Goodnessof-Fit Test. Contingency Tables. r x c Tables with Fixed Row or Column Totals. Other Applications. Summary and Concluding Remarks. 15. Nonparametric Statistics. Introduction. A General Two-Sampling Shift Model. A Sign Test for a Matched Pairs Experiment. The Wilcoxon Signed-Rank Test for a Matched Pairs Experiment. The Use of Ranks for Comparing Two Population Distributions: Independent Random Samples. The MannWhitney U Test: Independent Random Samples. The Kruskal-Wallis Test for One-Way Layout. The Friedman Test for Randomized Block Designs. The Runs Test: A Test for Randomness. Rank Correlation Coefficient. Some General Comments on Nonparametric Statistical Test. 16. Introduction to Bayesian Methods for Inference. Introduction. Bayesian Priors, Posteriors and Estimators. Bayesian Credible Intervals. Bayesian Tests of Hypotheses. Summary and Additional Comments. Appendix 1. Matrices and Other Useful Mathematical Results. Matrices and Matrix Algebra. Addition of Matrices. Multiplication of a Matrix by a Real Number. Matrix Multiplication. Identity Elements. The Inverse of a Matrix. The Transpose of a Matrix. A Matrix Expression for a System of Simultaneous Linear Equations. Inverting a Matrix. Solving a System of Simultaneous Linear Equations. Other Useful Mathematical Results. Appendix 2. Common Probability Distributions, Means, Variances, and Moment-Generating Functions. Discrete Distributions. Continuous Distributions. Appendix 3. Tables. Binomial Probabilities. Table of e-x. Poisson Probabilities. Normal Curve Areas. Percentage Points of the t Distributions. Percentage Points of the F Distributions. Distribution of Function U. Critical Values of T in the Wilcoxon Matched-Pairs, Signed-Ranks Test. Distribution of the Total Number of Runs R in Sample Size (n1,n2); P(R < a). Critical Values of Pearman’’s Rank Correlation Coefficient. Random Numbers. Answer to www.cengageasia.com 38 FEATURES Exercises. Index. • The authors efficiently use mathematics as a necessary tool to promote a firm understanding of statistical techniques. • Stressing connectivity, the authors explain not only how major topics play a role in statistical inference but also how the topics are related to one another. These integrating discussions appear most frequently in chapter introductions and conclusions. • This text takes a practical approach in both the exercises throughout and the useful topics in statistical methodology covered in the last five chapters. • Exercises are based on real data or actual experimental scenarios which allow students to see the practical uses of various statistical and probabilistic methods. © 2008, 944pp, Hardback, 9780495110811 MATHEMATICAL STATISTICS WITH APPLICATIONS, INTERNATIONAL EDITION, 7E CONTENTS Dennis Wackerly, University of Florida; William Mendenhall, University of Florida, Emeritus; Richard L. Scheaffer, University of Florida, Emeritus In their bestselling MATHEMATICAL STATISTICS WITH APPLICATIONS, premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid foundation in statistical theory while conveying the relevance and importance of the theory in solving practical problems in the real world. The authors’ use of practical applications and excellent exercises helps students discover the nature of statistics and understand its essential role in scientific research. NEW TO THIS EDITION • A new chapter introducing Bayesian Methods for Inference (Chapter 16). • A collection of new applets allows students to further build their knowledge and understanding of probability and inference with interactive examples of key concepts. Many applets have associated exercises, allowing instructors to easily integrate the applets into students’ coursework. • Some of the applets permit determination of probabilities and quantiles associated with normal, gamma, beta, t, F and Chi-square distributions. • With other applets, students can assess the effect of different parameter choices on the shape of gamma and beta density functions. • Hundreds of new and updated exercises throughout the book complement the existing exercises. 1. What Is Statistics? Introduction. Characterizing a Set of Measurements: Graphical Methods. Characterizing a Set of Measurements: Numerical Methods. How Inferences Are Made. Theory and Reality. Summary. 2. Probability. Introduction. Probability and Inference. A Review of Set Notation. A Probabilistic Model for an Experiment: The Discrete Case. Calculating the Probability of an Event: The Sample-Point Method. Tools for Counting Sample Points. Conditional Probability and the Independence of Events. Two Laws of Probability. Calculating the Probability of an Event: The Event-Composition Methods. The Law of Total Probability and Bayes’’’’s Rule. Numerical Events and Random Variables. Random Sampling. Summary. 3. Discrete Random Variables and Their Probability Distributions. Basic Definition. The Probability Distribution for Discrete Random Variable. The Expected Value of Random Variable or a Function of Random Variable. The Binomial Probability Distribution. The Geometric Probability Distribution. The Negative Binomial Probability Distribution (Optional). The Hypergeometric Probability Distribution. Moments and Moment-Generating Functions. Probability-Generating Functions (Optional). Tchebysheff’’’’s Theorem. Summary. 4. Continuous Random Variables and Their Probability Distributions. Introduction. The Probability Distribution for Continuous Random Variable. The Expected Value for Continuous Random Variable. The Uniform Probability Distribution. The Normal Probability Distribution. The Gamma Probability Distribution. The Beta Probability Distribution. Some General Comments. www.cengageasia.com 39 Other Expected Values. Tchebysheff’’’’s Theorem. Expectations of Discontinuous Functions and Mixed Probability Distributions (Optional). Summary. 5. Multivariate Probability Distributions. Introduction. Bivariate and Multivariate Probability Distributions. Independent Random Variables. The Expected Value of a Function of Random Variables. Special Theorems. The Covariance of Two Random Variables. The Expected Value and Variance of Linear Functions of Random Variables. The Multinomial Probability Distribution. The Bivariate Normal Distribution (Optional). Conditional Expectations. Summary. 6. Functions of Random Variables. Introductions. Finding the Probability Distribution of a Function of Random Variables. The Method of Distribution Functions. The Methods of Transformations. Multivariable Transformations Using Jacobians. Order Statistics. Summary. 7. Sampling Distributions and the Central Limit Theorem. Introduction. Sampling Distributions Related to the Normal Distribution. The Central Limit Theorem. A Proof of the Central Limit Theorem (Optional). The Normal Approximation to the Binomial Distributions. Summary. 8. Estimation. Introduction. The Bias and Mean Square Error of Point Estimators. Some Common Unbiased Point Estimators. Evaluating the Goodness of Point Estimator. Confidence Intervals. Large-Sample Confidence Intervals Selecting the Sample Size. SmallSample Confidence Intervals for u and u1-u2. Confidence Intervals for o2. Summary. 9. Properties of Point Estimators and Methods of Estimation. Introduction. Relative Efficiency. Consistency. Sufficiency. The Rao-Blackwell Theorem and Minimum-Variance Unbiased Estimation. The Method of Moments. The Method of Maximum Likelihood. Some Large-Sample Properties of MLEs (Optional). Summary. 10. Hypothesis Testing. Introduction. Elements of a Statistical Test. Common Large-Sample Tests. Calculating Type II Error Probabilities and Finding the Sample Size for the Z Test. Relationships Between Hypothesis Testing Procedures and Confidence Intervals. Another Way to Report the Results of a Statistical Test: Attained Significance Levels or p-Values. Some Comments on the Theory of Hypothesis Testing. Small-Sample Hypothesis Testing for u and u1-u2. Testing Hypotheses Concerning Variances. Power of Test and the NeymanPearson Lemma. Likelihood Ration Test. Summary. 11. Linear Models and Estimation by Least Squares. Introduction. Linear Statistical Models. The Method of Least Squares. Properties of the Least Squares Estimators for the Simple Linear Regression Model. Inference Concerning the Parameters BI. Inferences Concerning Linear Functions of the Model Parameters: Simple Linear Regression. Predicting a Particular Value of Y Using Simple Linear Regression. Correlation. Some Practical Examples. Fitting the Linear Model by Using Matrices. Properties of the Least Squares Estimators for the Multiple Linear Regression Model. Inferences Concerning Linear Functions of the Model Parameters: Multiple Linear Regression. Prediction a Particular Value of Y Using Multiple Regression. A Test for H0: Bg+1 + Bg+2 = . = Bk = 0. Summary and Concluding Remarks. 12. Considerations in Designing Experiments. The Elements Affecting the Information in a Sample. Designing Experiment to Increase Accuracy. The Matched Pairs Experiment. Some Elementary Experimental Designs. Summary. 13. The Analysis of Variance. Introduction. The Analysis of Variance Procedure. Comparison of More than Two Means: Analysis of Variance for a One-way Layout. An Analysis of Variance Table for a One-Way Layout. A Statistical Model of the One-Way Layout. Proof of Additivity of the Sums of Squares and E (MST) for a One-Way Layout (Optional). Estimation in the OneWay Layout. A Statistical Model for the Randomized Block Design. The Analysis of Variance for a Randomized Block Design. Estimation in the Randomized Block Design. Selecting the Sample Size. Simultaneous Confidence Intervals for More than One Parameter. Analysis of Variance Using Linear Models. Summary. 14. Analysis of Categorical Data. A Description of the Experiment. The Chi-Square Test. A Test of Hypothesis Concerning Specified Cell Probabilities: A Goodnessof-Fit Test. Contingency Tables. r x c Tables with Fixed Row or Column Totals. Other Applications. Summary and Concluding Remarks. 15. Nonparametric Statistics. Introduction. A General Two-Sampling Shift Model. A Sign Test for a Matched Pairs Experiment. The Wilcoxon Signed-Rank Test for a Matched Pairs Experiment. The Use of Ranks for Comparing Two Population Distributions: Independent Random Samples. The MannWhitney U Test: Independent Random Samples. The Kruskal-Wallis Test for One-Way Layout. The Friedman Test for Randomized Block Designs. The Runs Test: A Test for Randomness. Rank Correlation Coefficient. Some General Comments on Nonparametric Statistical www.cengageasia.com 40 Test. 16. Introduction to Bayesian Methods for Inference. Introduction. Bayesian Priors, Posteriors and Estimators. Bayesian Credible Intervals. Bayesian Tests of Hypotheses. Summary and Additional Comments. Appendix 1. Matrices and Other Useful Mathematical Results. Matrices and Matrix Algebra. Addition of Matrices. Multiplication of a Matrix by a Real Number. Matrix Multiplication. Identity Elements. The Inverse of a Matrix. The Transpose of a Matrix. A Matrix Expression for a System of Simultaneous Linear Equations. Inverting a Matrix. Solving a System of Simultaneous Linear Equations. Other Useful Mathematical Results. Appendix 2. Common Probability Distributions, Means, Variances, and Moment-Generating Functions. Discrete Distributions. Continuous Distributions. Appendix 3. Tables. Binomial Probabilities. Table of e-x. Poisson Probabilities. Normal Curve Areas. Percentage Points of the t Distributions. Percentage Points of the F Distributions. Distribution of Function U. Critical Values of T in the Wilcoxon Matched-Pairs, Signed-Ranks Test. Distribution of the Total Number of Runs R in Sample Size (n1,n2); P(R < a). Critical Values of Pearman’’s Rank Correlation Coefficient. Random Numbers. Answer to Exercises. Index. © 2008, 936pp, Paperback, 9780495385080 SAMPLING, 2E Design and Analysis, International Edition Sharon L. Lohr, Arizona State University Sharon L. Lohr’s SAMPLING: DESIGN AND ANALYSIS, 2e, International Edition provides a modern introduction to the field of survey sampling intended for a wide audience of statistics students. Practical and authoritative, the book is listed as a standard reference for training on real-world survey problems by a number of prominent surveying organizations. Lohr concentrates on the statistical aspects of taking and analyzing a sample, incorporating a multitude of applications from a variety of disciplines. The text gives guidance on how to tell when a sample is valid or not, and how to design and analyze many different forms of sample surveys. Recent research on theoretical and applied aspects of sampling is included, as well as optional technology instructions for using statistical software with survey data. NEW TO THIS EDITION • New Content—New chapters cover sampling rare populations, estimating a population size, and survey quality (which ties together much of the material in the earlier chapters). In addition, there is expanded treatment of computer-intensive methods such as jackknife and bootstrap, and discussion of new modes of data collection such as Internet surveys. • SAS integration—SAS is incorporated in examples for analyzing data from complex surveys, with SAS code provided on the book’s website. • Current research—Recent research in survey methodology is incorporated, enhancing the book’s real-world orientation. Examples include new approaches to linearization variance estimation, computer-intensive methods for variance estimation, small area estimation, nonresponse models, and plotting survey data.Current research—Recent research in survey methodology is incorporated, enhancing the book’s real-world orientation. Examples include new approaches to linearization variance estimation, computer-intensive methods for variance estimation, small area estimation, nonresponse models, and plotting survey data. • New applied examples—As is the case with most examples in the book, new ones are based on real surveys conducted in social sciences, epidemiology, and many other areas. • Varied, categorized exercises to build skills—Exercises, many of which are new, are categorized into four groups: A, Introductory Exercises, many suitable for hand calculations; B, Working with Survey Data, with most requiring use of statistical software; C, Working with Theory, ideal for more mathematically oriented classes; and D, Projects and Activities, with activities suitable either for classroom use or as assigned student projects. www.cengageasia.com 41 FEATURES • Flexibility in content and level—The book can be used for undergraduates who have had one introductory statistics course or for a first-year graduate course. • Use of real data—As much as possible, examples and exercises come from social sciences, engineering, agriculture, medicine, and other disciplines. • Topic coverage not found in other texts at this level— The book discusses analysis of complex surveys, nonresponse, and other important topics. These optional sections allow instructors the flexibility to pick and choose the additional topics they want to include with the core course content. • Emphasis on the importance of graphing the data— This helps students avoid flawed data analysis. CONTENTS 1. INTRODUCTION. A Sample Controversy. Requirements of a Good Sample. Selection Bias. Measurement Error. Questionnaire Design. Sampling and Nonsampling Errors. Exercises. 2. SIMPLE PROBABILITY SAMPLES. Types of Probability Samples. Framework for Probability Sampling. Simple Random Sampling. Sampling Weights. Confidence Intervals. Sample Size Estimation. Systematic Sampling. Randomization Theory Results for Simple Random Sampling. A Prediction Approach for Simple Random Sampling. When Should a Simple Random Sample Be Used? Chapter Summary. Exercises. 3. STRATIFIED SAMPLING. What Is Stratified Sampling? Theory of Stratified Sampling. Sampling Weights in Stratified Random Sampling. Allocating Observations to Strata. Defining Strata. Model-Based Inference for Stratified Sampling. Quota Sampling. Chapter Summary. Exercises. 4. RATIO AND REGRESSION ESTIMATION. Ratio Estimation in a Simple Random Sample. Estimation in Domains. Regression Estimation in Simple Random Sampling. Poststratification. Ratio Estimation with Stratified Samples. Model-Based Theory for Ratio and Regression Estimation. Chapter Summary. Exercises. 5. CLUSTER SAMPLING WITH EQUAL PROBABILITIES. Notation for Cluster Sampling. OneStage Cluster Sampling. Two-Stage Cluster Sampling. Designing a Cluster Sample. Systematic Sampling. Model-Based Inference in Cluster Sampling. Chapter Summary. Exercises. 6. SAMPLING WITH UNEQUAL PROBABILITIES. Sampling One Primary Sampling Unit. One-Stage Sampling with Replacement. TwoStage Sampling with Replacement. Unequal Probability Sampling Without Replacement. Examples of Unequal Probability Samples. Randomization Theory Results and Proofs. Models and Unequal Probability Sampling. Chapter Summary. Exercises. 7. COMPLEX SURVEYS. Assembling Design Components. Sampling Weights. Estimating a Distribution Function. Plotting Data from a Complex Survey. Univariate Plots. Design Effects. The National Crime Victimization Survey. Sampling and Experiment Design. Chapter Summary. Exercises. 8. NONRESPONSE. Effects of Ignoring Nonresponse. Designing Surveys to Reduce Nonsampling Errors. Callbacks and Two-Phase Sampling. Mechanisms for Nonresponse. Weighting Methods for Nonresponse. Imputation. Parametric Models for Nonresponse. What Is an Acceptable Response Rate? Chapter Summary. Exercises. 9. VARIANCE ESTIMATION IN COMPLEX SURVEYS. Linearization (Taylor Series) Methods. Random Group Methods. Resampling and Replication Methods. Generalized Variance Functions. Confidence Intervals. Chapter Summary. Exercises. 10. CATEGORICAL DATA ANALYSIS IN COMPLEX SURVEYS. Chi-Square Tests with Multinomial Sampling. Effects of Survey Design on Chi-Square Tests. Corrections to x2 Tests. Loglinear Models. Chapter Summary. Exercises. 11. REGRESSION WITH COMPLEX SURVEY DATA. Model-Based Regression in Simple Random Samples. Regression in Complex Surveys. Should Weights Be Used in Regression? Mixed Models for Cluster Samples. Logistic Regression. Generalized Regression Estimation for Population Totals. Chapter Summary. Exercises. 12. TWO-PHASE SAMPLING. Theory for Two-Phase Sampling. Two-Phase Sampling with Stratification. Two-Phase Sampling with Ratio Estimation. Subsampling Nonrespondents. Designing a Two-Phase Sample. Chapter Summary. Exercises. 13. ESTIMATING POPULATION SIZE. Capture-Recapture Estimates. Contingency Tables for Capture-Recapture Experiments. Assessing Undercoverage. Chapter Summary. Exercises. 14. RARE POPULATIONS AND SMALL AREA ESTIMATIONS. Sampling for Rare Events. Small Area Estimation. Chapter Summary. Exercises. 15. SURVEY QUALITY. Nonresponse Error. Measurement Error. Sensitive Questions. Processing Error. Sampling Error. Interaction of Error Sources. The Future of Sampling. Chapter Summary. Exercises. APPENDICES: PROBABILITY CONCEPTS USED IN SAMPLING. Probability. Random Variables and Expected Value. www.cengageasia.com 42 Conditional Probability. Conditional Expectation. REFERENCES. © 2010, 608pp, Paperback, 9780495110842 SAMPLING, 2E Design and Analysis Sharon L. Lohr, Arizona State University Sharon L. Lohr’s SAMPLING: DESIGN AND ANALYSIS, 2ND EDITION, provides a modern introduction to the field of survey sampling intended for a wide audience of statistics students. Practical and authoritative, the book is listed as a standard reference for training on real-world survey problems by a number of prominent surveying organizations. Lohr concentrates on the statistical aspects of taking and analyzing a sample, incorporating a multitude of applications from a variety of disciplines. The text gives guidance on how to tell when a sample is valid or not, and how to design and analyze many different forms of sample surveys. Recent research on theoretical and applied aspects of sampling is included, as well as optional technology instructions for using statistical software with survey data. NEW TO THIS EDITION • New Content--New chapters cover sampling rare populations, estimating a population size, and survey quality (which ties together much of the material in the earlier chapters). In addition, there is expanded treatment of computer-intensive methods such as jackknife and bootstrap, and discussion of new modes of data collection such as Internet surveys. • SAS integration--SAS is incorporated in examples for analyzing data from complex surveys, with SAS code provided on the book’s website. • Current research--Recent research in survey methodology is incorporated, enhancing the book’s real-world orientation. Examples include new approaches to linearization variance estimation, computer-intensive methods for variance estimation, small area estimation, nonresponse models, and plotting survey data. • New applied examples--As is the case with most examples in the book, new ones are based on real surveys conducted in social sciences, epidemiology, and many other areas. • Varied, categorized exercises to build skills--Exercises, many of which are new, are categorized into four groups: A, Introductory Exercises, many suitable for hand calculations; B, Working with Survey Data, with most requiring use of statistical software; C, Working with Theory, ideal for more mathematically oriented classes; and D, Projects and Activities, with activities suitable either for classroom use or as assigned student projects. FEATURES • Varied, categorized exercises to build skills--Exercises, many of which are new, are categorized into four groups: A, Introductory Exercises, many suitable for hand calculations; B, Working with Survey Data, with most requiring use of statistical software; C, Working with Theory, ideal for more mathematically oriented classes; and D, Projects and Activities, with activities suitable either for classroom use or as assigned student projects. • Use of real data--As much as possible, examples and exercises come from social sciences, engineering, agriculture, medicine, and other disciplines. • Topic coverage not found in other texts at this level-The book discusses analysis of complex surveys, nonresponse, and other important topics. These optional sections allow instructors the flexibility to pick and choose the additional topics they want to include with the core course content. • Emphasis on the importance of graphing the data-This helps students avoid flawed data analysis. CONTENTS 1. INTRODUCTION. A Sample Controversy. Requirements of a Good Sample. Selection Bias. Measurement Error. Questionnaire Design. Sampling and Nonsampling Errors. Exercises. 2. SIMPLE PROBABILITY SAMPLES. Types of Probability Samples. Framework for Probability Sampling. Simple Random Sampling. Sampling www.cengageasia.com 43 Weights. Confidence Intervals. Sample Size Estimation. Systematic Sampling. Randomization Theory Results for Simple Random Sampling. A Prediction Approach for Simple Random Sampling. When Should a Simple Random Sample Be Used? Chapter Summary. Exercises. 3. STRATIFIED SAMPLING. What Is Stratified Sampling? Theory of Stratified Sampling. Sampling Weights in Stratified Random Sampling. Allocating Observations to Strata. Defining Strata. Model-Based Inference for Stratified Sampling. Quota Sampling. Chapter Summary. Exercises. 4. RATIO AND REGRESSION ESTIMATION. Ratio Estimation in a Simple Random Sample. Estimation in Domains. Regression Estimation in Simple Random Sampling. Poststratification. Ratio Estimation with Stratified Samples. Model-Based Theory for Ratio and Regression Estimation. Chapter Summary. Exercises. 5. CLUSTER SAMPLING WITH EQUAL PROBABILITIES. Notation for Cluster Sampling. OneStage Cluster Sampling. Two-Stage Cluster Sampling. Designing a Cluster Sample. Systematic Sampling. Model-Based Inference in Cluster Sampling. Chapter Summary. Exercises. 6. SAMPLING WITH UNEQUAL PROBABILITIES. Sampling One Primary Sampling Unit. One-Stage Sampling with Replacement. TwoStage Sampling with Replacement. Unequal Probability Sampling Without Replacement. Examples of Unequal Probability Samples. Randomization Theory Results and Proofs. Models and Unequal Probability Sampling. Chapter Summary. Exercises. 7. COMPLEX SURVEYS. Assembling Design Components. Sampling Weights. Estimating a Distribution Function. Plotting Data from a Complex Survey. Univariate Plots. Design Effects. The National Crime Victimization Survey. Sampling and Experiment Design. Chapter Summary. Exercises. 8. NONRESPONSE. Effects of Ignoring Nonresponse. Designing Surveys to Reduce Nonsampling Errors. Callbacks and Two-Phase Sampling. Mechanisms for Nonresponse. Weighting Methods for Nonresponse. Imputation. Parametric Models for Nonresponse. What Is an Acceptable Response Rate? Chapter Summary. Exercises. 9. VARIANCE ESTIMATION IN COMPLEX SURVEYS. Linearization (Taylor Series) Methods. Random Group Methods. Resampling and Replication Methods. Generalized Variance Functions. Confidence Intervals. Chapter Summary. Exercises. 10. CATEGORICAL DATA ANALYSIS IN COMPLEX SURVEYS. Chi-Square Tests with Multinomial Sampling. Effects of Survey Design on Chi-Square Tests. Corrections to x2 Tests. Loglinear Models. Chapter Summary. Exercises. 11. REGRESSION WITH COMPLEX SURVEY DATA. Model-Based Regression in Simple Random Samples. Regression in Complex Surveys. Should Weights Be Used in Regression? Mixed Models for Cluster Samples. Logistic Regression. Generalized Regression Estimation for Population Totals. Chapter Summary. Exercises. 12. TWO-PHASE SAMPLING. Theory for Two-Phase Sampling. Two-Phase Sampling with Stratification. Two-Phase Sampling with Ratio Estimation. Subsampling Nonrespondents. Designing a Two-Phase Sample. Chapter Summary. Exercises. 13. ESTIMATING POPULATION SIZE. Capture-Recapture Estimates. Contingency Tables for Capture-Recapture Experiments. Assessing Undercoverage. Chapter Summary. Exercises. 14. RARE POPULATIONS AND SMALL AREA ESTIMATIONS. Sampling for Rare Events. Small Area Estimation. Chapter Summary. Exercises. 15. SURVEY QUALITY. Nonresponse Error. Measurement Error. Sensitive Questions. Processing Error. Sampling Error. Interaction of Error Sources. The Future of Sampling. Chapter Summary. Exercises. APPENDICES: PROBABILITY CONCEPTS USED IN SAMPLING. Probability. Random Variables and Expected Value. Conditional Probability. Conditional Expectation. REFERENCES. © 2010, 608pp, Hardback, 9780495105275, www.cengageasia.com 44 FEATURES SURVEY SAMPLING, INTERNATIONAL EDITION, 7E Richard L. Scheaffer, University of Florida, Emeritus; William Mendenhall, III, University of Florida, Emeritus; R. Lyman Ott; Kenneth G. Gerow, University of Wyoming SURVEY SAMPLING, 7th Edition, International Edition introduces students to the design and analysis of sample surveys via a practical, engaging approach. First, this introductory text begins with brief chapters focused on the important role that sample surveys play in the modern world. Then, each successive chapter builds on this foundation. These chapters start with the problem, describe the methodology needed for solving the problem, and provide the details of the estimation procedure using a compact presentation of the necessary formulas. Each chapter then works out the practical example in full detail. Finally, at the end of each chapter, SURVEY SAMPLING, 7th Edition, International Edition includes a wealth of exercises that enable students to continue practicing and to stretch their grasp of the content. NEW TO THIS EDITION • Integrates new software: By using software that significantly decreases the need for long arithmetic, the authors have allowed students to focus less on arithmetic, and instead on the valid use of appropriate statistical tools. • New Icons: Excel Tools icons have been added to the boxed equations and example solutions, so as to direct students to use Excel when appropriate. • More concise data: The data for examples and exercises is now more concise and is also incorporated with the electronic supplements. • Updated Appendices: the Appendices for this edition have been updated to be more concise and userfriendly. • Bridges the gap between classroom and practice in two significant ways: First, select sections on weights in unequal probability sampling. Then, they expanded the treatment of nonresponse in Chapter 11, including the use of imputation as a technique for handling some types of nonresponse. • Emphasizes two cutting-edge sampling techniques: the bootstrap, a modern technique for establishing margins of error and confidence intervals in complex designs, and an adaptive sampling technique for improving estimates while the field work is in progress. • Incorporates compatible computations: SURVEY SAMPLING, 7th Edition, International Edition’s computations are compatible with modern statistical software, using much fewer hand calculation formulas. • Utilizes detailed simulations: Key statistical concepts are demonstrated using step-by-step simulations. • Emphasizes practical aspects: topics include sources of errors in surveys, methods of data collection, designing questionnaires, and guidelines for planning surveys. • Expands Student Understanding: The “Experiences with Real Data” sections at the end of most chapters enable students to work with both large and small projects, some requiring computations to be handled by a computer, giving them valuable experience. CONTENTS 1. INTRODUCTION. 2. ELEMENTS OF THE SAMPLING PROBLEM. Introduction. Technical Terms. How to Select the Sample: The Design of the Sample Survey. Sources of Errors in Surveys. Designing a Questionnaire. Planning a Survey. Summary. 3. SOME BASIC CONCEPTS OF STATISTICS. Introduction. Summarizing Information in Populations and Samples: The Infinite Population Case. Summarizing Information in Populations and Samples: The Finite Population Case. Sampling Distributions. Covariance and Correlation. Estimation. Summary. 4. SIMPLE RANDOM SAMPLING. Introduction. How to Draw a Simple Random Sample. Estimation of a Population Mean and Total. Selecting the Sample Size for Estimating Population Means and Totals. Estimation of a Population Proportion. Comparing Estimates. Summary. 5. STRATIFIED RANDOM SAMPLING. Introduction. How to Draw a Stratified Random Sample. Estimation of a Population Mean and Total. Selecting www.cengageasia.com 45 the Sample Size for Estimating Population Means and Totals. Allocation of the Sample. Estimation of a Population Proportion. Selecting the Sample Size and Allocating the Sample to Estimate Proportions. Additional Comments on Stratified Sampling. An Optimal Rule for Choosing Strata. Stratification after Selection of the Sample. Double Sampling for Stratification. Summary. 6. RATIO, REGRESSION, AND DIFFERENCE ESTIMATION. Introduction. Surveys that Require the Use of Ratio Estimators. Ratio Estimation Using Simple Random Sampling. Selecting the Sample Size. Ratio Estimation in Stratified Random Sampling. Regression Estimation. Difference Estimation. Relative Efficiency of Estimators. Summary. 7. SYSTEMATIC SAMPLING. Introduction. How to Draw a Systematic Sample. Estimation of a Population Mean and Total. Estimation of a Population Proportion. Selecting the Sample Size. Repeated Systematic Sampling. Further Discussion of Variance Estimators. Summary. 8. CLUSTER SAMPLING. Introduction. How to Draw a Cluster Sample. Estimation of a Population Mean and Total. Equal Cluster Sizes; Comparison to Simple Random Sampling. Selecting the Sample Size for Estimating Population Means and Totals. Estimation of a Population Proportion. Selecting the Sample Size for Estimating Proportions. Cluster Sampling Combined with Stratification. Cluster Sampling with Probabilities Proportional to Size. Summary. 9. TWO-STAGE CLUSTER SAMPLING. Introduction. How to Draw a Two-Stage Cluster Sample. Unbiased Estimation of a Population Mean and Total. Ratio Estimation of a Population Mean. Estimation of a Population Proportion. Sampling Equal-Sized Clusters. Two-Stage Cluster Sampling with Probabilities Proportional to Size. Summary. 10. ESTIMATING THE POPULATION SIZE. Introduction. Estimation of a Population Size Using Direct Sampling. Estimation of a Population Size Using Inverse Sampling. Choosing Sample Sizes for Direct and Inverse Sampling. Estimating Population Density and Size from Quadrat Samples. Estimating Population Density and Size from Stocked Quadrats. Adaptive Sampling. Summary. 11. SUPPLEMENTAL TOPICS. Introduction. Interpenetrating Subsamples. Estimation of Means and Totals over Subpopulations. Random-Response Model. Use of Weights in Sample Surveys. Adjusting for Nonresponse. Imputation. Selecting the Number of Callbacks. The Bootstrap. Summary. 12. SUMMARY. Summary of the Designs and Methods. Comparisons among the Designs and Methods. Appendices. References and Bibliography Tables. Derivation of Some Main Results. Macros for MINITAB. Macros for SAS. Data Sets. Selected Answers. Index. © 2012, 448pp, Paperback, 9781111569341 Introductory Statistics APPLIED STATISTICS FOR ENGINEERS AND SCIENTISTS, 3E Jay L. Devore, California Polytechnic State University, San Luis Obispo; Nicholas R. Farnum, California State University, Fullerton; Jimmy A. Doi, California Polytechnic State University, San Luis Obispo This concise book for engineering and sciences students emphasizes modern statistical methodology and data analysis. APPLIED STATISTICS FOR ENGINEERS AND SCIENTISTS is ideal for one-term courses that cover probability only to the extent that it is needed for inference. The authors emphasize application of methods to real problems, with real examples throughout. The text is designed to meet ABET standards and has been updated to reflect the most current methodology and practice. Available with InfoTrac® Student Collections http://gocengage.com/ infotrac. NEW TO THIS EDITION • New exercises and examples, based on real data and information from published sources, reinforce a practical, realistic approach that helps students relate to and understand statistical concepts better. • Computer output has been updated to reflect the latest technology. www.cengageasia.com 46 FEATURES • The integration of the “Six Sigma Terminology” in Chapter 6 adds to the text’s modern approach. • Describe the key features (e.g. a chapter in the TOC, a feature to spotlight, a supplement to call out, and technology to mention) instructors will want to be aware of. • Examples that use real data from industry reports and articles introduce students to real-world situations while they learn statistical concepts. • The authors cover all the important topics concisely, giving students a solid understanding of both statistical methods and design with a problem-solving focus. • The authors emphasize modern statistical methods including quality and design of experiments to give students exposure to practical applications. • An emphasis on graphical data analysis methods is consistent with the authors’ computer-integrated approach. • Practical computer pedagogy is integrated throughout the book so that learning of concepts can focus on real applications, using output from the most widely used statistical packages, such as MINITAB, JMP IN, SAS, and S-Plus. • Numerous relevant, current exercises and examples appear throughout. CONTENTS 1. DATA AND DISTRIBUTIONS. Populations, Samples and Processes. Visual Displays for Univariate Data. Describing Distributions. The Normal Distribution. Other Continuous Distributions. Several Useful Discrete Distributions. Supplementary Exercises. Bibliography. 2. NUMERICAL SUMMARY MEASURES. Measures of Center. Measures of Variability. More Detailed Summary Quantities. Quantile Plots. Supplementary Exercises. Bibliography. 3. BIVARIATE AND MULTIVARIATE DATA AND DISTRIBUTIONS. Scatter Plots. Correlation. Fitting a Line to Bivariate Data. Nonlinear Relationships. Using More Than One Predictor. Joint Distributions. Supplementary Exercises. Bibliography. 4. OBTAINING DATA. Operational Definitions. Data from Sampling. Data from Experiments. Measurement Systems. Supplementary Exercises. Bibliography. 5. PROBABILITY AND SAMPLING DISTRIBUTIONS. Chance Experiments. Probability Concepts. Conditional Probability and Independence. Random Variables. Sampling Distributions. Describing Sampling Distributions. Supplementary Exercises. Bibliography. 6. QUALITY CONTROL. Terminology. How Control Charts Work. Control Charts for Mean and Variance. Process Capability Analysis. Control Charts for Attribute Data. Reliability. Supplementary Exercises. Bibliography. 7. ESTIMATION AND STATISTICAL INTERVALS. Point Estimation. Large-Sample Confidence Intervals for a Population Mean. More Large-Sample Confidence Intervals. Small-Sample Intervals Based on a Normal Population Distribution. Intervals for µ1-µ2 Based on a Normal Population Distributions. Other Topics in Estimation (Optional). Supplementary Exercises. Bibliography. 8. TESTING STATISTICAL HYPOTHESES. Hypotheses and Test Procedures. Tests Concerning Hypotheses About Means. Tests Concerning Hypotheses About a Categorical Population. Testing the Form of a Distribution. Further Aspects of Hypothesis Testing. Supplementary Exercises. Bibliography. 9. THE ANALYSIS OF VARIANCE. Terminology and Concepts. Single-Factor ANOVA. Interpreting ANOVA Results. Randomized Block Experiments. Supplementary Exercises. Bibliography. 10. EXPERIMENTAL DESIGN. Terminology and Concepts. Two-Factor Designs. Multifactor Designs. 2k Designs. Fractional Factorial Designs. Supplementary Exercises. Bibliography. 11. INFERENTIAL METHODS IN REGRESSION AND CORRELATION. Regression and Models Involving a Single Independent Variable. Inferences About the Slope Coefficient ß. Inferences Based on the Estimated Regression Line. Multiple Regression Models. Inferences in Multiple Regression. Further Aspects of Regression Analysis. Supplementary Exercises. Bibliography. APPENDIX TABLES. ANSWERS TO ODD-NUMBERED EXERCISES. INDEX. © 2014, 656pp, Hardback, 9781133111368 www.cengageasia.com 47 ELEMENTARY STATISTICS Looking at the Big Picture Nancy Pfenning, University of Pittsburgh Using a successfully class-tested approach that gives coherence to a broad range of introductory topics, this innovative text provides students with a real-world, big picture view of statistics as well as problemsolving strategies that can be applied to the statistical questions, real data, and examples that they will encounter. Author Nancy Pfenning organizes content around four basic processes of statistics: producing data, displaying and summarizing data, understanding probability, and using probability to perform statistical inference. Within this framework, the book progresses systematically through five basic problem situations involving values of variables (quantitative, categorical, or a blend). As a result, students learn to identify which situation applies and how to choose the correct display, summary, or inference tool or technique. As students gain proficiency in specific statistical techniques, the author also points out connections among topics and techniques. More than 1,000 real-life examples and categorized exercises support the approach, engaging students in practicing and developing a variety of skills. FEATURES • Big picture problem-solving approach--An innovative approach organizes content around four processes of statistics and the basic situations that apply to any statistical problem. Students learn specific techniques while gaining a broad perspective on statistics along with strategies for choosing the appropriate technique for any research situation. • Flexible content and technology integration--The presentation of each topic is self-contained enough to allow for more advanced topics such as ANOVA or chi-square to be skipped if desired with no loss in course coherence. A “Using Software” section near the end of each chapter makes it easy for instructors to emphasize the use of software in the course, or choose not to do so. • Examples using real data--Hundreds of well-chosen examples, based on current data from a wide variety of statistical applications, clarify the presentation of ideas. Each example follows a consistent format: Background, Question, Response, and Practice Exercise. Data sets feature values that allow easy entering by hand into a computer or calculator; they are also available at the Book Companion Website. • Plentiful categorized exercises, computational and conceptual--A set of problems after each major section assures that students practice basic concepts as they are introduced. A more comprehensive set at the end of each chapter (“Warming Up,” “Exploring the Big Picture,” “Using Software,” “Discovering Research,” and “Reporting on Research”) encourages students to integrate individual topics. Like the examples, exercises cover a variety of topics and use real data. • Emphasis on connections among topics--Strategically placed marginal notes show how each new topic fits into the larger framework of statistics, relating it to material covered earlier as well as to content that follows. These connections can be digested by students at their own pace, without disrupting the flow of main ideas. CONTENTS 1. Introduction: Variables and Processes in Statistics. Types of Variables: Categorical or Quantitative. Students Talk Stats: Identifying Types of Variables. Handling. Data for Two Types of Variables. Roles of Variables: Explanatory or Response. Statistics as a Four-Stage Process. PART I: DATA PRODUCTION. 2. Sampling: Which Individuals Are Studied. Sources of Bias in Sampling: When Selected Individuals Are Not Representative. Probability Sampling Plans: Relying on Randomness. Role of Sample Size: Bigger Is Better if the Sample Is Representative. From Sample to Population: To What Extent Can We Generalize? Students Talk Stats: Seeking a Representative Sample. 3. Design: How Individuals Are Studied. Various Designs for Studying Variables. Sample Surveys: When Individuals Report Their Own Values. Observational Studies: When Nature Takes Its Course. Experiments: When Researchers Take www.cengageasia.com 48 Control. Students Talk Stats: Does TV Cause ADHD? Considering Study Design. PART II: DISPLAYING AND SUMMARIZING DATA. 4. Displaying and Summarizing Data for a Single Variable. Single Categorical Variable. Students Talk Stats: Biased Sample, Biased Assessment. Single Quantitative Variables and the Shape of a Distribution. Center and Spread: What’s Typical for Quantitative Values, and How They Vary. Normal Distributions: The Shape of Things to Come. 5. Displaying and Summarizing Relationships. Relationship Between One Categorical and One Quantitative Variable. Students Talk Stats: Displaying and Summarizing Paired Data. Relationship Between Two Categorical Variables. Relationships Between Two Quantitative Variables. Students Talk Stats: How Outliers and Influential Observations Affect a Relationship. Students Talk Stats: Confounding in a Relationship Between Two Quantitative Variables. PART III: PROBABILITY. 6. Finding Probabilities. The Meaning of “Probability” and Basic Rules. More General Probability Rules and Conditional Probability. Students Talk Stats: Probability as a Weighted Average of Conditional Probabilities. 7. Random Variables. Discrete Random Variables. Binomial Random Variables. Students Talk Stats: Calculating and Interpreting the Mean and Standard Deviation of Count or Proportion. Continuous Random Variables and the Normal Distribution. Students Talk Stats: Means, Standard Deviations, and Below-Average Heights. 8. Sampling Distributions. The Behavior of Sample Proportion in Repeated Random Samples. The Behavior of Sample Mean in Repeated Random Samples. Students Talk Stats: When Normal Approximations Are Appropriate. PART IV: STATISTICAL INFERENCE. 9. Inference for a Single Categorical Variable. Point Estimate and Confidence Interval: A Best Guess and a Range of Plausible Values for Population Proportion. Students Talk Stats: Interpreting a Confidence Interval. Test: Is a Proposed Population Proportion Plausible? Students Talk Stats: Interpreting a P-value. Students Talk Stats: What Type of Error Was Made? Students Talk Stats: The Correct Interpretation of a Small P-value. Students Talk Stats: The Correct Interpretation When a P-value Is Not Small. 10. Inference for a Single Quantitative Variable. Inference for a Mean when Population Standard Deviation Is Known or Sample Size Is Large. Students Talk Stats: Confidence Interval for a Mean. Students Talk Stats: Interpreting a Confidence Interval for the Mean Correctly. Inference for a Mean When the Population Standard Deviation Is Unknown and the Sample Size Is Small. Students Talk Stats: Practical Application of a t Test. A Closer Look at Inference for Means. 11. Inference for Relationships Between Categorical and Quantitative Variables. Inference for a Paired Design with t. Inference for a Two-Sample Design with t. Students Talk Stats: Ordinary Vs. Pooled TwoSample t. Inference for a Several-sample Design with F: Analysis of Variance. Students Talk Stats: Reviewing Relationships between Categorical and Quantitative Variables. 12. Inference for Relationships Between Two Categorical Variables. Comparing Proportions with a z Test. Comparing Counts with a Chi-Square Test. 13. Inference for Relationships Between Two Quantitative Variables. Inference for Regression: Focus on the Slope of the Regression Line. Students Talk Stats: No Evidence of a Relationship. Interval Estimates for an Individual or Mean Response. 14. How Statistics Problems Fit into the Big Picture. The Big Picture in Problem-Solving. Students Talk Stats: Choosing the Appropriate Statistical Tools. 15. Non-Parametric Methods (Online). The Sign Test as an Alternative to the Paired t Test. The Rank-Sum Test as an Alternative to the Two-Sample t Test. Summary of Non-Parametrics. 16. Two-Way ANOVA (Online). 17. Multiple Regression (Online). PART V: SOLUTIONS TO SELECTED EXERCISES. © 2011, 784pp, Hardback, 9780495016526 www.cengageasia.com 49 ELEMENTARY STATISTICS Looking at the Big Picture, International Edition Nancy Pfenning, University of Pittsburgh Using a successfully class-tested approach that gives coherence to a broad range of introductory topics, this innovative text provides students with a real-world, big picture view of statistics as well as problemsolving strategies that can be applied to the statistical questions, real data, and examples that they will encounter. Author Nancy Pfenning organizes content around four basic processes of statistics: producing data, displaying and summarizing data, understanding probability, and using probability to perform statistical inference. Within this framework, the book progresses systematically through five basic problem situations involving values of variables (quantitative, categorical, or a blend). As a result, students learn to identify which situation applies and how to choose the correct display, summary, or inference tool or technique. As students gain proficiency in specific statistical techniques, the author also points out connections among topics and techniques. More than 1,000 real-life examples and categorized exercises support the approach, engaging students in practicing and developing a variety of skills. FEATURES • Big picture problem-solving approach—An innovative approach organizes content around four processes of statistics and the basic situations that apply to any statistical problem. Students learn specific techniques while gaining a broad perspective on statistics along with strategies for choosing the appropriate technique for any research situation. • Flexible content and technology integration—The presentation of each topic is self-contained enough to allow for more advanced topics such as ANOVA or chi-square to be skipped if desired with no loss in course coherence. A “Using Software” section near the end of each chapter makes it easy for instructors to emphasize the use of software in the course, or choose not to do so. • Examples using real data—Hundreds of well-chosen examples, based on current data from a wide variety of statistical applications, clarify the presentation of ideas. Each example follows a consistent format: Background, Question, Response, and Practice Exercise. Data sets feature values that allow easy entering by hand into a computer or calculator; they are also available at the Book Companion Website. • Plentiful categorized exercises, computational and conceptual—A set of problems after each major section assures that students practice basic concepts as they are introduced. A more comprehensive set at the end of each chapter (“Warming Up,” “Exploring the Big Picture,” “Using Software,” “Discovering Research,” and “Reporting on Research”) encourages students to integrate individual topics. Like the examples, exercises cover a variety of topics and use real data. • Emphasis on connections among topics—Strategically placed marginal notes show how each new topic fits into the larger framework of statistics, relating it to material covered earlier as well as to content that follows. These connections can be digested by students at their own pace, without disrupting the flow of main ideas. CONTENTS 1. Introduction: Variables and Processes in Statistics. Types of Variables: Categorical or Quantitative. Students Talk Stats: Identifying Types of Variables. Handling. Data for Two Types of Variables. Roles of Variables: Explanatory or Response. Statistics as a Four-Stage Process. PART I: DATA PRODUCTION. 2. Sampling: Which Individuals Are Studied. Sources of Bias in Sampling: When Selected Individuals Are Not Representative. Probability Sampling Plans: Relying on Randomness. Role of Sample Size: Bigger Is Better if the Sample Is Representative. From Sample to Population: To What Extent Can We Generalize? Students Talk Stats: Seeking a Representative Sample. 3. Design: How Individuals Are Studied. Various Designs for Studying Variables. Sample Surveys: When Individuals Report Their Own Values. Observational Studies: When Nature Takes Its Course. Experiments: When Researchers Take www.cengageasia.com 50 Control. Students Talk Stats: Does TV Cause ADHD? Considering Study Design. PART II: DISPLAYING AND SUMMARIZING DATA. 4. Displaying and Summarizing Data for a Single Variable. Single Categorical Variable. Students Talk Stats: Biased Sample, Biased Assessment. Single Quantitative Variables and the Shape of a Distribution. Center and Spread: What’s Typical for Quantitative Values, and How They Vary. Normal Distributions: The Shape of Things to Come. 5. Displaying and Summarizing Relationships. Relationship Between One Categorical and One Quantitative Variable. Students Talk Stats: Displaying and Summarizing Paired Data. Relationship Between Two Categorical Variables. Relationships Between Two Quantitative Variables. Students Talk Stats: How Outliers and Influential Observations Affect a Relationship. Students Talk Stats: Confounding in a Relationship Between Two Quantitative Variables. PART III: PROBABILITY. 6. Finding Probabilities. The Meaning of “Probability” and Basic Rules. More General Probability Rules and Conditional Probability. Students Talk Stats: Probability as a Weighted Average of Conditional Probabilities. 7. Random Variables. Discrete Random Variables. Binomial Random Variables. Students Talk Stats: Calculating and Interpreting the Mean and Standard Deviation of Count or Proportion. Continuous Random Variables and the Normal Distribution. Students Talk Stats: Means, Standard Deviations, and Below-Average Heights. 8. Sampling Distributions. The Behavior of Sample Proportion in Repeated Random Samples. The Behavior of Sample Mean in Repeated Random Samples. Students Talk Stats: When Normal Approximations Are Appropriate. PART IV: STATISTICAL INFERENCE. 9. Inference for a Single Categorical Variable. Point Estimate and Confidence Interval: A Best Guess and a Range of Plausible Values for Population Proportion. Students Talk Stats: Interpreting a Confidence Interval. Test: Is a Proposed Population Proportion Plausible? Students Talk Stats: Interpreting a P-value. Students Talk Stats: What Type of Error Was Made? Students Talk Stats: The Correct Interpretation of a Small P-value. Students Talk Stats: The Correct Interpretation When a P-value Is Not Small. 10. Inference for a Single Quantitative Variable. Inference for a Mean when Population Standard Deviation Is Known or Sample Size Is Large. Students Talk Stats: Confidence Interval for a Mean. Students Talk Stats: Interpreting a Confidence Interval for the Mean Correctly. Inference for a Mean When the Population Standard Deviation Is Unknown and the Sample Size Is Small. Students Talk Stats: Practical Application of a t Test. A Closer Look at Inference for Means. 11. Inference for Relationships Between Categorical and Quantitative Variables. Inference for a Paired Design with t. Inference for a Two-Sample Design with t. Students Talk Stats: Ordinary Vs. Pooled TwoSample t. Inference for a Several-sample Design with F: Analysis of Variance. Students Talk Stats: Reviewing Relationships between Categorical and Quantitative Variables. 12. Inference for Relationships Between Two Categorical Variables. Comparing Proportions with a z Test. Comparing Counts with a Chi-Square Test. 13. Inference for Relationships Between Two Quantitative Variables. Inference for Regression: Focus on the Slope of the Regression Line. Students Talk Stats: No Evidence of a Relationship. Interval Estimates for an Individual or Mean Response. 14. How Statistics Problems Fit into the Big Picture. The Big Picture in Problem-Solving. Students Talk Stats: Choosing the Appropriate Statistical Tools. 15. Non-Parametric Methods (Online). The Sign Test as an Alternative to the Paired t Test. The Rank-Sum Test as an Alternative to the Two-Sample t Test. Summary of Non-Parametrics. 16. Two-Way ANOVA (Online). 17. Multiple Regression (Online). PART V: SOLUTIONS TO SELECTED EXERCISES. © 2011, 608pp, Paperback, 9780495831457 www.cengageasia.com 51 ELEMENTARY STATISTICS, 11E Robert R. Johnson, Monroe Community College; Patricia J. Kuby In their own classrooms, through their popular texts, and in the conferences they lead, Robert Johnson and Patricia Kuby have inspired hundreds of thousands of students and their instructors to see the utility and practicality of statistics. Now in its Eleventh Edition, ELEMENTARY STATISTICS has been consistently praised by users and reviewers for its clear exposition and relevant examples, exercises, and applications. A focus on technology to help students succeed-including MINITAB®, Excel®, and TI-83/84 output and instructions throughout--is enhanced by a wealth of supplements that save instructors time and give students interactive guidance and support. All this and more have established this text’s reputation for being remarkably accessible for students to learn from--and simple and straightforward for instructors to teach from. NEW TO THIS EDITION • New chapter-opening vignettes--More than 50% of the book’s chapter-opening vignettes, each of which focuses on an everyday aspect of life, are new. Illustrated with statistical information, each chapter opener provides a relevant, familiar context for students’ initial step into the concepts covered in the chapter. • New applied examples--Nearly 20% of the text’s applied examples are new or updated to help engage student interest. Enhanced step-by-step solutions present key statistical concepts in a clear progression. • Over 20% new and updated exercises--21% of the exercises are new or updated to reflect current events and other timely topics that involve the use of statistics. • Chapter 6, “Normal Probability Distributions,” has been completely rewritten to present the Standard Normal Distribution utilizing the cumulative area approach, incorporating a more intuitive idea with respect to the total area under a curve and following more closely the format used with calculators and statistical software. To support this change, a new, corresponding two-page Table 3, “Cumulative Areas of the Standard Normal Distribution,” is included among the tables at the back of the text. • Statistics CourseMate--This new online resource provides a rich array of interactive and supplementary material to accompany the text, including an eBook version of the text. For more information, see the full description under Supplements. • Solution Builder for ELEMENTARY STATISTICS, 11th Edition--This new instructor resource provides fully worked-out solutions to all text exercises in customizable online format. For more information, see the full description under Supplements. • Aplia™ online homework for ELEMENTARY STATISTICS, 11th Edition--Aplia is a premium online homework system dedicated to improving learning by increasing student effort and engagement. For more information, see the full description under Supplements. FEATURES • Emphasis on interpretation of statistical information and real applications--Starting in Chapter 1, when students learn key terms and procedures; in Chapter 4, “Probability,” where analysis rather than formula is highlighted; and continuing throughout the text, the authors emphasize the role of interpretation in statistical analysis. Examples and exercises feature real applications of statistics, and chapter-opening vignettes enhance the relevance of the material for students. Critical thinking exercises throughout chapters further support the book’s practical, proven approach. • Skillbuilder Applet exercises and interactive video skillbuilders--These exercises give students increased opportunities for hands-on learning and shift their attention from simply memorizing formulas and concepts to actively internalizing the behavior of concepts and interpreting data. Created to help students build the foundational skills they need, the online videos provide step-by-step instruction, walking students through key examples from the text. www.cengageasia.com 52 • Flexible organization and integrated, up-to-date technology instructions--The p-value and classical approaches to hypothesis testing are initially introduced separately and thereafter presented sideby-side, to both emphasize their comparability and allow a wide range of teaching approaches. Similarly, descriptive regression and correlation are covered early (Chapter 3), and MINITAB®, Excel®, and the TI-83/84 graphing calculator instructions are found throughout the text instead of being relegated to end-of-chapter material or appendices. CONTENTS Part I: DESCRIPTIVE STATISTICS. 1. Statistics. 2. Descriptive Analysis and Presentation of Single-Variable Data. 3. Descriptive Analysis and Presentation of Bivariate Data. Part II: PROBABILITY. 4. Probability. 5. Probability Distributions (Discrete Variables). 6. Normal Probability Distributions. 7. Sample Variability. Part III: INFERENTIAL STATISTICS. 8. Introduction to Statistical Inferences. 9. Inferences Involving One Population. 10. Inferences Involving Two Populations. Part IV: MORE INFERENTIAL STATISTICS. 11. Applications of ChiSquare. 12. Analysis of Variance. 13. Linear Correlation and Regression. 14. Elements of Nonparametric Statistics. Appendix A: Basic Principles of Counting. Appendix B: Tables. Introductory Concepts. Review Lessons. Answers to Odd-Numbered Exercises. Answers to Chapter Practice Tests. Index for Applications. Index for Computer and Calculator Instructions. Index. © 2012, 832pp, Hardback, 9780538733502 ELEMENTARY STATISTICS, ENHANCED REVIEW EDITION (W/CD-ROM & CENGAGENOW, INFOTRAC® 2-SEM., ILRN HOMEWORK, PERSONAL TUTOR, INTERNET COMPANION FOR STATISTICS 2-SEM. PAC), 10E Robert R. Johnson, Monroe Community College; Patricia J. Kuby In their own classrooms, through their popular texts, and in the conferences they lead, Bob Johnson and Pat Kuby have inspired hundreds of thousands of students and their instructors to see the utility and practicality of statistics. Robert Johnson and Patricia Kuby’s ELEMENTARY STATISTICS, Tenth Edition has been consistently praised by users and reviewers for its clear exposition and relevant examples, exercises, and applications. Technology enhancements include the addition of Video Skillbuilders and StatisticsNow™ (part of the CengageNOW suite of technology products), our personalized online learning companion. This increased focus on technology to help students succeed, along with the wealth of instructor supplements and flexibility of technology coverage (with MINITAB, Excel, and TI-83 output and instructions throughout) clearly differentiate this text from its competitors as the most accessible text for students to learn from and the most straightforward text for instructors to teach from. NEW TO THIS EDITION • The new introductory concepts, review lessons, and solutions located in the back of the text written by author, Patricia Kuby, provide your students with the help they need to grasp various algebraic and basic statistical concepts. • New Learning Outcomes have been added to the endof-chapter material. These outcomes relate to specific section material and exercises and allow students to www.cengageasia.com 53 diagnose their mastery of a topic. • StatisticsNow™ (part of the CengageNOW suite of technology products). Featured within chapters, StatisticsNow is a robust, personalized online learning companion that helps students gauge their own unique study needs and makes the most of their study time by building focused Personalized Learning Plans that reinforce key concepts. Pre-Tests give students an initial assessment of their knowledge. Personalized Learning Plans, based on the students’ answers to the pre-test questions, outline key elements for review. Post-Tests assess students’ mastery of core concepts for each chapter. Access to StatisticsNow is available at no additional charge with purchase of a new text. • Interactive Video Skillbuilders contain hours of helpful, interactive video instruction. These videos walk your students through key examples from the text, step by stepgiving them a foundation in the skills that they need to know. Video icons located in the margin guide students to view the video on the Skillbuilder CD-ROM. • New technology output has been added throughout the text, reflecting the latest changes to MINITAB, Excel, and TI-83/84 graphing calculator output. • 30% of the exercises are new and many others have been significantly updated. Additional critical thinking exercises have been infused throughout the exercise sets. In addition, Classic Exercises can still be found on the CD-ROM accompanying the text, providing more opportunity to assign practice exercises. CONTENTS Part I: DESCRIPTIVE STATISTICS. 1. Statistics. Americans, Here’’s Looking At You. What is Statistics? Measurability and Variability. Data Collection. Comparison of Probability and Statistics. Statistics and Technology. 2. Descriptive Analysis and Presentation of Single-Variable Data. You and the Internet. Graphical Presentation of Data. Graphs, Pareto Diagrams, and Stem-And-Leaf Displays. Frequency Distributions and Histograms. Numerical Descriptive Statistics. Measures of Central Tendency. Measures of Dispersion. Measures of Position. Interpreting and Understanding Standard Deviation. The Art of Statistical Deception. Mean and Standard Deviation of Frequency Distribution (Optional). 3. Descriptive Analysis and Presentation of Bivariate Data. The Kid is All Grown Up. Bivariate Data. Linear Correlation. Linear Regression. Part II: PROBABILITY. 4. Probability. Sweet Statistics. Probability of Events. Conditional Probability of Events. Rules of Probability. Mutually Exclusive Events. Independent Events. Mutually Exclusive, Independent Events—A Relationship? 5. Probability Distributions (Discrete Variables). Caffeine Drinking. Random Variables. Probability Distribution of a Discrete Random Variable. Mean and Variance of a Discrete Probability Distribution. The Binomial Probability Distribution. Mean and Standard Deviation of the Binomial Distribution. 6. Normal Probability Distributions. Intelligence Scores. Normal Probability Distributions. The Standard Normal Distribution. Applications of Normal Distributions. Notation. Normal Approximation of the Binomial. 7. Sample Variability. 275 Million Americans. Sampling Distributions. The Sampling Distribution of Sample Means. Application of the Sampling Distribution of Sample Means. Part III: INFERENTIAL STATISTICS. 8. Introduction to Statistical Inferences. Were They Shorter Back Then? The Nature of Estimation. Estimation of a Mean (ó known). The Nature of Hypothesis Testing. Hypothesis Test of Mean ì (ó Known): A Probability Value Approach. Hypothesis Test of Mean ì (ó Known): A Classical Approach. 9. Inferences Involving One Population. Get Enough Daily Exercise? Inferences About Mean ì (ó Unknown). Inferences About the Binomial Probability of Success. Inferences About Variance and Standard Deviation. 10. Inferences Involving Two Populations. Students, Credit Cards and Debt. Independent and Dependent Samples. Inferences Concerning the Mean Difference Using Two Dependent Samples. Inferences Concerning the Difference Between Means Using Two Independent Samples. Inferences Concerning the Difference Between Proportions Using Two Independent Samples. Inferences Concerning the Ratio of Variances Using Two Independent Samples. Part IV: MORE INFERENTIAL STATISTICS. 11. Applications of Chi-Square. Cooling a Great Hot Taste. Chi-Square Statistic. Inferences Concerning Multinomial Experiments. Inferences Concerning Contingency Tables. 12. Analysis of Variance. Time Spent Commuting to Work. Introduction to the Analysis of Variance Technique. The Logic Behind ANOVA. Applications of Single-Factor ANOVA. 13. Linear Correlation and Regression. Beautiful Golden Wheat! Linear Correlation Analysis. Inferences About the Linear Correlation Coefficient. Linear Regression Analysis. Inferences Concerning the Slope of the Regression Line. Confidence www.cengageasia.com 54 Interval Estimates For Regression. Understanding the Relationship Between Correlation and Regression. 14. Elements of Nonparametric Statistics. Teenagers’’ Attitudes. Nonparametric Statistics. Comparing Statistical Tests. The Sign Test. The Mann-Whitney U Test. The Runs Test. Rank Correlation. Appendix A: Basic Principles of Counting. Appendix B: Tables. Answers to Odd-Numbered Exercises. Answers to Chapter Practice Tests. Index for Applications. Index for Computer and Calculator Instructions. Index. Credits. Formula Card. © 2008, 832pp, Hardback, 9780495383864 ELEMENTARY STATISTICS, INTERNATIONAL EDITION, 11E Robert R. Johnson, Monroe Community College; Patricia J. Kuby In their own classrooms, through their popular texts, and in the conferences they lead, Robert Johnson and Patricia Kuby have inspired hundreds of thousands of students and their instructors to see the utility and practicality of statistics. Now in its Eleventh Edition, ELEMENTARY STATISTICS, International Edition has been consistently praised by users and reviewers for its clear exposition and relevant examples, exercises, and applications. A focus on technology to help students succeed—including MINITAB®, Excel®, and TI-83/84 output and instructions throughout—is enhanced by a wealth of supplements that save instructors time and give students interactive guidance and support. All this and more have established this text’s reputation for being remarkably accessible for students to learn from—and simple and straightforward for instructors to teach from. NEW TO THIS EDITION • New chapter-opening vignettes—More than 50% of the book’s chapter-opening vignettes, each of which focuses on an everyday aspect of life, are new. Illustrated with statistical information, each chapter opener provides a relevant, familiar context for students’ initial step into the concepts covered in the chapter. • New applied examples—Nearly 20% of the text’s applied examples are new or updated to help engage student interest. Enhanced step-by-step solutions present key statistical concepts in a clear progression. • Over 20% new and updated exercises—21% of the exercises are new or updated to reflect current events and other timely topics that involve the use of statistics.The text’s more than 1700 exercises provide a wealth of practice problems, with each exercise set including a range of exercise types that progress from basic recall to multi-step to items requiring critical thinking. As always, most exercises can be calculated either by hand or by using technology. • Chapter 6, “Normal Probability Distributions,” has been completely rewritten to present the Standard Normal Distribution utilizing the cumulative area approach, incorporating a more intuitive idea with respect to the total area under a curve and following more closely the format used with calculators and statistical software. To support this change, a new, corresponding two-page Table 3, “Cumulative Areas of the Standard Normal Distribution,” is included among the tables at the back of the text. • Statistics CourseMate—This new online resource provides a rich array of interactive and supplementary material to accompany the text, including an eBook version of the text. For more information, see the full description under Supplements. • Solution Builder for ELEMENTARY STATISTICS, 11e, International Edition—This new instructor resource provides fully worked-out solutions to all text exercises in customizable online format. For more information, see the full description under Supplements. • Aplia™ online homework for ELEMENTARY STATISTICS, 11e, International Edition—Aplia is a premium online homework system dedicated to improving learning by increasing student effort and engagement. For more information, see the full description under Supplements. FEATURES • Emphasis on interpretation of statistical information www.cengageasia.com 55 and real applications—Starting in Chapter 1, when students learn key terms and procedures; in Chapter 4, “Probability,” where analysis rather than formula is highlighted; and continuing throughout the text, the authors emphasize the role of interpretation in statistical analysis. Examples and exercises feature real applications of statistics, and chapter-opening vignettes enhance the relevance of the material for students. Critical thinking exercises throughout chapters further support the book’s practical, proven approach. • Skillbuilder Applet exercises and interactive video skillbuilders—These exercises give students increased opportunities for hands-on learning and shift their attention from simply memorizing formulas and concepts to actively internalizing the behavior of concepts and interpreting data. Created to help students build the foundational skills they need, the online videos provide step-by-step instruction, walking students through key examples from the text. • Flexible organization and integrated, up-to-date technology instructions—The p-value and classical approaches to hypothesis testing are initially introduced separately and thereafter presented sideby-side, to both emphasize their comparability and allow a wide range of teaching approaches. Similarly, descriptive regression and correlation are covered early (Chapter 3), and MINITAB®, Excel®, and the TI-83/84 graphing calculator instructions are found throughout the text instead of being relegated to end-of-chapter material or appendices. CONTENTS Part I: DESCRIPTIVE STATISTICS. 1. Statistics. 2. Descriptive Analysis and Presentation of Single-Variable Data. 3. Descriptive Analysis and Presentation of Bivariate Data. Part II: PROBABILITY. 4. Probability. 5. Probability Distributions (Discrete Variables). 6. Normal Probability Distributions. 7. Sample Variability. Part III: INFERENTIAL STATISTICS. 8. Introduction to Statistical Inferences. 9. Inferences Involving One Population. 10. Inferences Involving Two Populations. Part IV: MORE INFERENTIAL STATISTICS. 11. Applications of ChiSquare. 12. Analysis of Variance. 13. Linear Correlation and Regression. 14. Elements of Nonparametric Statistics. Appendix A: Basic Principles of Counting. Appendix B: Tables. Introductory Concepts. Review Lessons. Answers to Odd-Numbered Exercises. Answers to Chapter Practice Tests. Index for Applications. Index for Computer and Calculator Instructions. Index. © 2012, 832pp, Paperback, 9780840058782 FUNDAMENTALS OF BIOSTATISTICS, 7E Bernard Rosner, Harvard University and Harvard Medical School Bernard Rosner’s FUNDAMENTALS OF BIOSTATISTICS is a practical introduction to the methods, techniques, and computation of statistics with human subjects. It prepares students for their future courses and careers by introducing the statistical methods most often used in medical literature. Rosner minimizes the amount of mathematical formulation (algebra-based) while still giving complete explanations of all the important concepts. As in previous editions, a major strength of this book is that every new concept is developed systematically through completely worked out examples from current medical research problems. NEW TO THIS EDITION • New sections on Parametric Survival Analysis and Parametric Regression Models for Survival Data. • New sections on Attributable Risk, Extensions to Logistic Regression, and Longitudinal Data Analysis. • New section on Interval Estimation for Rank Correlation Coefficients. • New material on Regression Models for Clustered Binary Data. • More than 250 new and updated problems. FEATURES • Complete online data sets for SAS, SPSS, JMP, MINITAB, Excel, and ASCII formats, covering all problems in the text that make use of real data. • Review problems for quick practice before the main exercise sets. www.cengageasia.com 56 • Large number of applications highlighted in the Index of Applications on the endsheets (they are grouped by medical specialty and summarize the examples and exercises that address each application). Includes cutting-edge research in fields that students will actually be working in later in their academic and professional careers. Many of the applications are based on original research conducted by the author. • Brief solutions for more than 300 problems are given in text and approximately 600 additional problems and complete solutions are given in the Study Guide available on the Companion Website. CONTENTS 1. General Overview. 2. Descriptive Statistics. 3. Probability. 4. Discrete Probability Distributions. 5. Continuous Probability Distributions. 6. Estimation. 7. Hypothesis Testing: One-Sample Inference. 8. Hypothesis Testing: Two-Sample Inference. 9. Nonparametric Methods. 10. Hypothesis Testing: Categorical Data. 11. Regression and Correlation Methods. 12. Multisample Inference. 13. Design and Analysis Techniques for Epidemiologic Studies. 14. Hypothesis Testing: PersonTime Data. © 2011, 888pp, Hardback, 9780538733496 (algebra-based) while still giving complete explanations of all the important concepts. As in previous editions, a major strength of this book is that every new concept is developed systematically through completely worked out examples from current medical research problems. NEW TO THIS EDITION • New sections on Parametric Survival Analysis and Parametric Regression Models for Survival Data. • New sections on Attributable Risk, Extensions to Logistic Regression, and Longitudinal Data Analysis. • New section on Interval Estimation for Rank Correlation Coefficients. • New material on Regression Models for Clustered Binary Data. • More than 250 new and updated problems. FEATURES • Complete online data sets for SAS, SPSS, JMP, MINITAB, Excel, and ASCII formats, covering all problems in the text that make use of real data. • Review problems for quick practice before the main exercise sets. • Large number of applications highlighted in the Index of Applications on the endsheets (they are grouped by medical specialty and summarize the examples and exercises that address each application). Includes cutting-edge research in fields that students will actually be working in later in their academic and professional careers. Many of the applications are based on original research conducted by the author. • Brief solutions for more than 300 problems are given in text and approximately 600 additional problems and complete solutions are given in the Study Guide available on the Companion Website. CONTENTS FUNDAMENTALS OF BIOSTATISTICS, INTERNATIONAL EDITION, 7E Bernard Rosner, Harvard University and Harvard Medical School Bernard Rosner’s FUNDAMENTALS OF BIOSTATISTICS, 7e, International Edition is a practical introduction to the methods, techniques, and computation of statistics with human subjects. It prepares students for their future courses and careers by introducing the statistical methods most often used in medical literature. Rosner minimizes the amount of mathematical formulation 1. General Overview. 2. Descriptive Statistics. 3. Probability. 4. Discrete Probability Distributions. 5. Continuous Probability Distributions. 6. Estimation. 7. Hypothesis Testing: One-Sample Inference. 8. Hypothesis Testing: Two-Sample Inference. 9. Nonparametric Methods. 10. Hypothesis Testing: Categorical Data. 11. Regression and Correlation Methods. 12. Multisample Inference. 13. Design and Analysis Techniques for Epidemiologic Studies. 14. Hypothesis Testing: PersonTime Data. © 2011, 888pp, Paperback, 9780538735896 www.cengageasia.com 57 Available answers. An icon in the text margin identifies concepts and applications for which an applet is available on the website. • Technology Today features at the end of each chapter offer Microsoft® Excel 2010 and MINITAB outputs and instructions, giving students hands-on experience performing statistical analysis with these widely used programs. FEATURES INTRODUCTION TO PROBABILITY AND STATISTICS, INTERNATIONAL EDITION, 14E William Mendenhall, University of Florida, Emeritus; Robert J. Beaver, University of California, Riverside; Barbara M. Beaver, University of California, Riverside Used by hundreds of thousands of students since its first edition, INTRODUCTION TO PROBABILITY AND STATISTICS, 14E, International Edition continues to blend the best of its proven, error-free coverage with new innovations. Written for the higher end of the traditional introductory statistics market, the book takes advantage of modern technology—including computational software and interactive visual tools—to facilitate statistical reasoning as well as the interpretation of statistical results. In addition to showing how to apply statistical procedures, the authors explain how to describe real sets of data meaningfully, what the statistical tests mean in terms of their practical applications, how to evaluate the validity of the assumptions behind statistical tests, and what to do when statistical assumptions have been violated. The new edition retains the statistical integrity, examples, exercises, and exposition that have made this text a market leader—and builds upon this tradition of excellence with new technology integration. NEW TO THIS EDITION • The book’s website, Statistics CourseMate, includes a Personal Trainer feature that offers further explanation of some of the more difficult concepts through detailed definitions, step-by-step hints on problem solving, self-correcting exercises, and data sets for text exercises. Applets demonstrate key concepts so they are easily visualized, accompanied by exercises that provide practice in the required statistical methods and immediate feedback with • Exercises: The book includes more than 1,300 exercises, many of which are new or updated for this edition. New topics for the book’s chapter-ending case study exercises include “How to Save Money for Groceries,” “School Accountability Study: How Is Your School Doing?” and “Are You Going to Curve the Grades?” • Market leader: The integrity of the statistics and the quality of the examples and exercises keep this text in the bestseller category. Comprehensive in coverage, it delivers a more rigorous offering with traditional coverage of probability. Instructors and students alike appreciate its error-free material and exercises, and its clear exposition. • Real data: The first to incorporate case studies and real data, this text continues to set the standard. Many examples and exercises use authentic data sets, helping students see the connections between their studies and their lives. • Quick reference: At the end of each chapter, Key Concepts and Formulas sections provide quick reference for students, helping them ensure they are well prepared for assignments and tests. CONTENTS Introduction: What Is Statistics? 1. Describing Data with Graphs. 2. Describing Data with Numerical Measures. 3. Describing Bivariate Data. 4. Probability and Probability Distributions. 5. Several Useful Discrete Distributions. 6. The Normal Probability Distribution. 7. Sampling Distributions. 8. Large-Sample Estimation. 9. Large-Sample Tests of Hypotheses. 10. Inference from Small Samples. 11. The Analysis of Variance. 12. Linear Regression and Correlation. 13. Multiple Regression Analysis. 14. Analysis of Categorical Data. 15. Nonparametric Statistics. Appendix I: Tables. Answers to Selected Exercises. www.cengageasia.com 58 © 2013, 744pp, Paperback, 9789814568890 INTRODUCTION TO STATISTICS AND DATA ANALYSIS, 4E Roxy Peck, California Polytechnic State University; Chris Olsen, George Washington High School, Cedar Rapids, Iowa; Jay L. Devore, California Polytechnic State University, San Luis Obispo Roxy Peck, Chris Olsen, and Jay Devore’s new edition uses real data and attention-grabbing examples to introduce students to the study of statistics and data analysis. Traditional in structure yet modern in approach, this text guides students through an intuition-based learning process that stresses interpretation and communication of statistical information. Simple notation--including the frequent substitution of words for symbols--helps students grasp concepts and cement their comprehension. Handson activities and interactive applets allow students to practice statistics firsthand. INTRODUCTION TO STATISTICS AND DATA ANALYSIS, 4th Edition, includes updated coverage of the graphing calculator as well as expanded coverage of probability. NEW TO THIS EDITION • New Cumulative Review Exercises appear after selected chapter sets, allowing you to test students’ comprehension of topics spanning multiple chapters. • More than 50 new examples and more than 270 new exercises, which use data from current newspapers and journals, help students understand statistical concepts in a realistic context. In addition, more of the exercises specifically ask students to write (for example, by requiring them to explain their reasoning, interpret results, and comment on important features of an analysis). • Examples and exercises using data sets that can be accessed online from the text website (Statistics CourseMate) are designated by an icon, as are examples that are further illustrated in technology manuals for MINITAB®, SPSS®, etc.--which are also available at the website. • Exercises have been added to the “Interpreting and Communicating the Results of Statistical Analyses” sections, giving students the chance to practice these important skills. All of these sections now have assignable end-of-section questions associated with them. • Approximately 90 exercises have video solutions, presented by Brian Kotz of Montgomery College, which can be viewed online or downloaded for viewing later. An icon designates these exercises in the text. • A greater number of end-of-chapter activities have been added to the book, ideal for use as a chapter capstone or integrated at appropriate places as the chapter material is covered in class. • The “Graphing Calculator Explorations” have been significantly revised and updated. FEATURES • The text is enhanced by a variety of online teaching and learning resources. These include the book’s website, Statistics CourseMate; Aplia™, an online interactive learning solution that improves comprehension of key statistical concepts; and Cengage Learning’s Enhanced WebAssign®, which allows you to assign problems from the text online and ensure that students receive multimedia tutorial support as they complete their assignments. • Most chapters contain “Graphing Calculator Explorations.” Found at the end of the chapter, these explorations promote statistical thinking and allow students to actively experience technology. • Optional material on logistic regression in Chapter 5, “Summarizing Bivariate Data,” assures complete coverage of this important topic. CONTENTS 1. The Role Of Statistics And The Data Analysis Process. 2. Collecting Data Sensibly. 3. Graphical Methods For Describing Data. 4. Numerical Methods For Describing Data. 5. Summarizing Bivariate Data. 6. Probability. 7. Random Variables And Probability Distributions. 8. Sampling Variability And Sampling Distribution. 10. Hypothesis Testing Using A Single Sample. 11. Comparing Two Populations Or Treatments. 12. The Analysis www.cengageasia.com 59 Of Categorical Data And Goodness-Of-Fit Tests. 13. Simple Linear Regression And Correlation: Inferential Methods. 14. Multiple Regression Analysis. 15. Analysis Of Variance. 16. Nonparametric (Distribution-Free Statistical Methods (Online). © 2012, 944pp, Hardback, 9780840054906 INTRODUCTION TO STATISTICS AND DATA ANALYSIS, ENHANCED REVIEW EDITION (WITH CENGAGENOW PRINTED ACCESS CARD), 3E Roxy Peck, California Polytechnic State University; Chris Olsen, George Washington High School, Cedar Rapids, Iowa; Jay L. Devore, California Polytechnic State University, San Luis Obispo Roxy Peck, Chris Olsen and Jay Devore’s new edition uses real data and attention-grabbing examples to introduce students to the study of statistics and data analysis. The Third Edition includes coverage of the graphing calculator and includes expanded coverage of probability. Traditional in structure yet modern in approach, this text guides students through an intuition-based learning process that stresses interpretation and communication of statistical information. It helps students grasp concepts and cement their comprehension by using simple notationfrequently substituting words for symbols. Hands-on activities and interactive applets allow students to practice statistics firsthand. This Enhanced Edition includes new Teaching Tips for each chapter in the book, specific references to other available instructor resources, and suggestions for effectively teaching an Advanced Placement Introduction to Statistics course. Also, Enhanced WebAssign now complements a robust supplement package. NEW TO THIS EDITION • Chapter Opening Teaching Tips have been added to beginning of each printed chapter in the Annotated Instructor’s Edition. Broken down section-by-section, these tips suggest additional assignments and activities for a section, as well as the best uses of the supplemental resources for the text (such as the Activity Workbook, Instructor’s Binder and Testbank) that could be included in a lesson plan. • Margin Teaching Tips have been integrated throughout the Annotated Instructor’s Edition. These tips are strategically placed at points where an instructor can integrate an activity, a clarifying example, a quiz to test students on concept formation, and thoughtprovoking questions to ask your students. • Easy to assign. Easy to use. Easy to manage. Enhanced WebAssign allows you to assign, collect, grade, and record homework assignments via the web and includes links to text-specific content, video examples, and problem-specific tutorials. Enchanced WebAssign is more than a homework system; it is a complete learning system for your students. FEATURES • Annotated Instructors Edition – designed specifically to aid you in teaching the course. • “Communicating and Interpreting the Results of Statistical Analysis” sections emphasize the importance of being able to interpret statistical output and communicate its meaning to non-statisticians. • Nearly 100 new examples and hundreds of new or updated exercises with current data from journals, newspapers, and other published sources have been added to this edition, helping students understand statistical concepts in a realistic, relevant context. There are now over 1,100 exercises in the third edition. • Eight new activities bring the total to 33. Found at the ends of chapters, these hands-on activities allow students to see statistics unfold before their eyes as they conduct their own experiments. • New optional material on logistic regression in Chapter 5, “Summarizing Bivariate Data,” assures complete coverage of this important topic. • This text continues to feature broad coverage of sampling, survey design and experimental design coverage of transformations and nonlinear regression; and an emphasis on graphical display as a necessary component of data analysis. www.cengageasia.com 60 CONTENTS 1. The Role Of Statistics And The Data Analysis Process. 2. Collecting Data Sensibly. 3. Graphical Methods For Describing Data. 4. Numerical Methods For Describing Data. 5. Summarizing Bivariate Data. 6. Probability. 7. Random Variables And Probability Distributions. 8. Sampling Variability And Sampling Distributions. 9. Estimation Using A Single Sample. 10. Hypothesis Testing Using A Single Sample. 11. Comparing Two Populations Or Treatments. 12. The Analysis Of Categorical Data And Goodness-Of-Fit Tests. 13. Simple Linear Regression And Correlation: Inferential Methods. 14. Multiple Regression Analysis. 15. Analysis Of Variance. Single-Factor Anova And The F Test. Multiple 16. Nonparametric Statistical Methods (Online). © 2009, 880pp, Hardback, 9780495557838 MIND ON STATISTICS, 2E Australian & New Zealand Jessica M. Utts, University of California, Irvine; Robert F. Heckard, Pennsylvania State University; Helen MacGillivray, Queensland University of Technology This text is adapted from Jessica Utts and Robert Heckard’s best-selling general introductory statistics text, Mind on Statistics. It emphasises the conceptual development of statistical ideas and seeks to find meaning in data. This local adaptation retains the conversational approach of the original text, with real examples and case studies of appeal and interest to Australian and New Zealand students. The revised structure and relevant examples motivate students and guide them through the statistical process of tackling problems, planning and data collection, analysing and interpreting information and finding solutions. NEW TO THIS EDITION statistical inference • NEW chapter signposts to help students navigate the important concepts • More discipline-specific examples to appeal to mixed cohorts of students • Updated local and international statistical developments, examples and case studies • Increased coverage of range of statistical software tips – Excel, SPSS, R and Minitab FEATURES • Substantial range of local and international examples and case studies of a scientific nature are targeted to engage students. These cases and examples offer instructors more relevant ways to demonstrate statistical concepts in class. • Compelling examples and “real life” case studies help to motivate and engage students in the topic. This reinforces the premise of the text that something is easier to learn if it can be related to something interesting or previously learned. • This local adaptation is structured to more smoothly follow the logical flow of statistics thinking in problem solving – from planning and data collection to analysis and interpretation, making it easier for students to understand statistics concepts in context. • NEW material in Chapter 4 introduces students to the concepts and practice of statistical inference and helps students to understand this important area of statistics. • Chapter 14: ‘Some further data situations’ extends on material introduced in early chapters and considers situations relating to binary logistic regression, and failure and survival data. CONTENTS 1. Thinking Statistically 2. Gathering and preparing useful data 3. Turning data into graphical information 4. Data features and summary statistics 5. Investigating categorical variables and their relationships 6. Probability essentials for data analysis 7. Estimating proportions with confidence 8. Analysis of variance 9. Regression 10. Interval estimation 11. Testing hypotheses about means, proportions and variances in one and two samples 12. More on probability, random variables and distributions 13. Sums and differences of random variables 14. Some further data situations • NEW – An introduction to concepts and practice of www.cengageasia.com 61 © 2014, 576pp, Paperback, 9780170227810 MIND ON STATISTICS, 5E Jessica M. Utts, University of California, Irvine; Robert F. Heckard, Pennsylvania State University MIND ON STATISTICS, Fifth Edition, emphasizes the conceptual development of statistical ideas and the importance of looking for-and finding-meaning in data. Authors Jessica Utts and Robert Heckard actively engage students’ natural curiosity, motivating them with intriguing questions and explaining statistical topics in the context of a wide range of interesting, useful examples and case studies. Throughout the text, the authors balance the promotion of statistical literacy with coverage of the statistical methodology taught in general introductory statistics courses. Their approach is based on the premises that new material is much easier to learn and remember if it is related to something interesting or previously known, and if students actively ask questions and find the answers for themselves. More than any other text available, MIND ON STATISTICS motivates and encourages students to develop their statistical intuition by focusing on analyzing data and interpreting results, rather than on mathematical formulation. NEW TO THIS EDITION • Increased integration of digital assets. • The book includes a significant number of Skillbuilder applets, which provide opportunities for in-class or independent hands-on exploration of key statistical concepts. The applets can be found on the book’s companion website. • Videos walk students through examples step by step, reinforcing key statistical concepts. • New examples and exercises were written for the new edition. All data in examples, case studies, and exercises also have been updated to the latest information available. • The number of “In Summary” boxes has been increased, and the boxes are placed more consistently throughout the chapters. A listing of the “In Summary” boxes now appears at the end of the chapter. • The language has been tightened and simplified whenever possible. • The book includes a larger art program to help with visualization. • Significant updates and modifications have been made to Chapter 7 on probability. FEATURES • To create a more natural flow of topic coverage, Chapters 5 and 6 precede Chapters 3 and 4 so that the material on describing data comes before the two chapters on collecting data. • Topics in this edition include multiple comparisons in Chapters 1 and 13, power curves in Chapters 12 and 13, and McNemar’s test in Chapter 15. CONTENTS 1. Statistics Success Stories and Cautionary Tales. 2. Turning Data into Information. 3. Relationships between Quantitative Variables. 4. Relationships between Categorical Variables. 5. Sampling: Surveys and How to Ask Questions. 6. Gathering Useful Data for Examining Relationships. 7. Probability. 8. Random Variables. 9. Understanding Sampling Distributions: Statistics as Random Variables. 10. Estimating Proportions with Confidence. 11. Estimating Means with Confidence. 12. Testing Hypotheses about Proportions. 13. Testing Hypotheses about Means. 14. Inference about Simple Regression. 15. More about Inference for Categorical Variables. 16. Analysis of Variance. 17. Turning Information into Wisdom. © 2015, 768pp, Hardback, 9781285463186 www.cengageasia.com 62 rotation, interaction plots, and more. • A chapter on Creating Reports is included and the new Minitab Assistant is introduced in several chapters. • In Release 16 almost everything in Minitab can be customized. You can create your own menus and toolbars, add short cuts, and so on. FEATURES MINITAB® HANDBOOK, INTERNATIONAL EDITION, 6E Barbara F. Ryan; Brian L. Joiner; Jonathan D. Cryer, University of Iowa Written by developers Barbara Ryan and Brian Joiner, and professor of statistics Jon Cryer, this text illustrates by example and step-by-step instruction how to use MINITAB 16 to explore and analyze data. The book introduces readers to MINITAB software while understanding that they may also be learning statistics at the same time. The authors illustrate how statistical concepts apply to data analysis through examples. More than 4,000 colleges, universities, and high schools rely on MINITAB, making this software the educational standard. Because MINITAB is the leading package used in industry for quality and process improvement, students who learn MINITAB in class will have the advantage of knowing how to use a real-world business tool. NEW TO THIS EDITION • MINITAB HANDBOOK: UPDATED FOR RELEASE 16, 6E, International Edition reflects substantial changes in the Minitab software and in statistics teaching that have occurred since the fifth edition was published. • Today’s students are most comfortable using a menu-driven interface, so the sixth edition teaches Minitab procedures using mostly the menus. Session commands, however, are introduced early on and referred to throughout the book so that students will learn that they can accomplish some procedures more efficiently with commands. • The book is based on Minitab, Release 16, first available in 2011. Release 16 is the most comprehensive release to date and includes substantial new features. • This edition makes significant use of Minitab’s latest graphics capabilities and enhances several features, such as plot editing, brushing and jittering, 3D graph • The sixth edition has been thoroughly updated to reflect Release 16 of the MINITAB software. • Those new to Windows and Minitab will find Chapter 1 helpful in introducing the menu interface and the data management techniques available with projects. • Chapter 2 introduces Session commands so that you will be prepared to use these commands later in the book when necessary. • Chapter 3 introduces all Worksheet operations in order to introduce students to the procedures they will need later on to carry out such simple operations as stacking columns or sorting data. CONTENTS 1. INTRODUCTION TO MINITAB. Welcome to Minitab. Working with Data. Working with Minitab Windows. Working with Minitab Projects. The Project Manager. 2. SESSION COMMANDS. The Minitab Command Language. Useful Session Commands. Working with the Contents of the Session Window. 3. WORKSHEET OPERATIONS. Editing the Worksheet. Manipulating Data. Importing and Exporting Data. 4. ONE-VARIABLE GRAPHS AND SUMMARIES. Understanding Variable Types. Exploring Graphs in Minitab. Creating Distribution Plots 102. Histograms 111. Descriptive Statistics. Creating a Boxplot. Summarizing and Displaying Categorical Data. 5. TWO- AND THREE-VARIABLE GRAPHS. Creating Scatterplots. Creating Plots with Groups. Creating Plots with Several Variables. Graph Editing and Annotation. Graphs with Three or More Variables. Creating Time Series Plots. 6. STATISTICAL DISTRIBUTIONS. The Normal Distribution. The Binomial Distribution. Normal Approximation to the Binomial. The Poisson Distribution. A Summary of the Theoretical Distributions in Minitab 7. SIMULATION. Learning about Randomness. Bernoulli Trials. Simulating Data from a Normal Distribution. Simulating Data from the Binomial Distribution. Sampling from Other Distributions. Sampling Actual Populations. The Base for the Random Number Generator. The Central Limit Theorem. 8. ONE- www.cengageasia.com 63 SAMPLE CONFIDENCE INTERVALS AND TESTS FOR MEANS. How Sample Means Vary. Confidence Interval for μ. Test of Hypothesis for μ. Confidence Intervals and Tests for Proportions. Departures from Assumptions. Hypothesis Tests with the Minitab Assistant 262 9. COMPARING TWO MEANS: CONFIDENCE INTERVALS AND TESTS. Paired and Independent Data. Difference Between Two Means: Paired Data. Difference Between Two Means. Two-Sample Procedures with the Minitab Assistant . 10. ANALYSIS OF VARIANCE. Analysis of Variance with One Factor. Analysis of Variance with Two Factors. Randomized Block Designs. Residuals and Fitted Values. 11. REGRESSION AND CORRELATION. Correlation. Simple Regression: Fitting a Straight Line. Making Inferences from Straight-Line Fits. Multiple Regression. Fitting Polynomials. Interpreting Residuals in Simple and Polynomial Regression. Using Transformations. Plotting Regression Lines, Planes and Surfaces. Regression with the Minitab Assistant. 12. CHI-SQUARE TESTS AND CONTINGENCY. Chi-Square Goodness-of-Fit Test. Contingency Tables. Making the Table and Computing Chi-Square. Tables with Small Expected Counts. 13. NONPARAMETRIC STATISTICS. Sign Procedures. Wilcoxon Signed Rank Procedures. Two-Sample Rank Procedures. Kruskal-Wallis Test. 14. CONTROL CHARTS. Control Charts and Process Variation. Control Charts for Measurement Data. Control Charts for Attribute Data. Control Charts with the Minitab Assistant. 15. ADDITIONAL TOPICS IN REGRESSION. Variable Selection in Regression. Diagnostics in Regression. Additional Regression Features. 16. ADDITIONAL TOPICS IN ANALYSIS OF VARIANCE. Multiple Comparisons with One-Way. Multifactor Balanced Designs. Unbalanced Designs. Analysis of Covariance. Random Effects and Mixed Models. 17. CREATING A STATISTICAL REPORT. Adding Session Output to the ReportPad. Adding a Graph to the ReportPad. Editing in ReportPad. Saving a Report. Copying a Report to a Word Processor. 18. MINITAB MACROS. Global Macros. Writing a Macro. Invoking a Macro. Variable Column and Row Numbers. Control Statements. Available Macros. Index. © 2013, 560pp, Paperback, 9781285175027, PRELIMINARY EDITION OF STATISTICS Learning from Data (with Printed Access Card for JMP) Roxy Peck, California Polytechnic State University STATISTICS: LEARNING FROM DATA, by respected and successful author Roxy Peck, resolves common problems faced by both students and instructors with an innovative approach to elementary statistics. Instead of assuming that students will “pick it up along the way,” Peck tackles the areas students struggle with most--probability, hypothesis testing, and selecting an appropriate method of analysis-unlike any text on the market. Probability coverage is based on current research that shows how students best learn the subject. Two unique chapters, one on statistical inference and another on learning from experiment data, address two common areas of student confusion: choosing a particular inference method and using inference methods with experimental data. Supported by learning objectives, real-data examples and exercises, and technology notes, this brand new text guides students in gaining conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice. FEATURES • A New Approach to Probability: Research has demonstrated how students develop an understanding of probability and chance. Using natural frequencies to reason about probability, especially conditional probability, is much easier for students to understand. The treatment of probability in this text is complete, including conditional probability and Bayes’ Rule type probability calculations. However, it’s done in a way that eliminates the need for the symbolism and formulas, which are a roadblock for many students. • Chapter on Overview of Statistical Inference (Chapter 7): This short chapter focuses on the things www.cengageasia.com 64 students need to think about in order to select an appropriate method of analysis. In most texts, these considerations are “hidden” in the discussion that occurs when a new method is introduced. Discussing these considerations up front in the form of four key questions that need to be answered before choosing an inference method makes it easier for students to make correct choices. • An Organization That Reflects the Data Analysis Process: Students are introduced early to the idea that data analysis is a process that begins with careful planning, followed by data collection, data description using graphical and numerical summaries, data analysis, and finally interpretation of results. The ordering of topics in the textbook mirrors this process: data collection, then data description, then statistical inference. • Inference for Proportions Before Inference for Means: The book makes it possible to develop the concept of a sampling distribution via simulation. Simulation is simpler in the context of proportions, where it is easy to construct a hypothetical population (versus the more complicated context of means, which requires assumptions about shape and spread.) In addition, inferential procedures for proportions are based on the normal distribution, allowing students to focus on the new concepts of estimation and hypothesis testing without having to grapple with the introduction of the t distribution. • Separate Treatment of Inference Based on Experiment Data (Chapter 14): Many statistical studies involve collecting data via experimentation. The same inference procedures used to estimate or test hypotheses about population parameters also are used to estimate or test hypotheses about treatment effects. However, the necessary assumptions are slightly different (for example, random assignment replaces the assumption of random selection), as is the wording of conclusions. Treating both cases together tends to confuse students; this text makes the distinction clear. • Chapter Learning Objectives--Keeping Students Informed about Expectations: The learning objectives explicitly state the expected student outcomes, and are presented in three categories: Conceptual Understanding, Mastery of Mechanics, and Putting It into Practice. • Preview--Motivation for Learning: Each chapter opens with a Preview and Preview Example that provide motivation for studying the concepts and methods introduced in the chapter. They address why the material is worth learning, provide the conceptual foundation for the methods covered in the chapter, and connect to what the student already knows. These relevant and current examples provide a context in which one or more questions are proposed for further investigation. The context is revisited in the chapter once students have the necessary understanding to more fully address the questions posed. • Real Data That Motivates and Engages: Examples and exercises with overly simple settings don’t allow students to practice interpreting results in real situations. The exercises and examples are a particular strength of this text. Most involve data extracted from journal articles, newspapers, and other published sources. They cover a wide range of disciplines and subject areas of interest to today’s students, including, among others, health and fitness, consumer research, psychology and aging, environmental research, law and criminal justice, and entertainment. • Exercises Organized into Developmental Sets to Structure the Out-of-Class Experience: • End-of-section exercises are presented in two developmental sets. The exercises in each set work together to assess all of the learning objectives for the section. Additional section exercises are included for those who want more practice. CONTENTS Section I: COLLECTING DATA. 1. Collecting Data in Reasonable Ways. Statistical Studies: Observation and Experimentation. Collecting Data: Planning an Observational Study. Collecting Data: Planning an Experiment. The Importance of Random Selection and Random Assignment: What Types of Conclusions are Reasonable? Section II: DESCRIBING DATA DISTRIBUTIONS. 2. Graphical Methods for Describing Data Distributions. Selecting an Appropriate Graphical Display. Displaying Categorical Data: Bar Charts and Comparative Bar Charts. Displaying Numerical Data: Dotplots, Stem-and-Leaf Displays, and Histograms. Displaying Bivariate Numerical Data: Scatterplots and Time-Series Plots. Graphical Displays in the Media. 3. Numerical Methods for Describing Data Distributions. Selecting Appropriate Numerical Summaries. Describing www.cengageasia.com 65 Center and Spread for Data Distributions that are Approximately Symmetric. Describing Center and Spread for Data Distributions that are Skewed or Have Outliers. Summarizing a Data Set: Boxplots. Measures of Relative Standing: z-scores and Percentiles. 4. Describing Bivariate Numerical Data. Correlation. Linear Regression: Fitting a Line to Bivariate Data. Assessing the Fit of a Line. Describing Linear Relationships and Making Predictions--Putting it all Together. Bonus Material on Logistic Regression (Online). Section III: A FOUNDATION FOR INFERENCE: REASONING ABOUT PROBABILITY. 5. Probability. Interpreting Probabilities. Computing Probabilities. Probabilities of More Complex Events: Unions, Intersections and Complements. Conditional Probability. Probability as a Basis for Making Decisions. Estimating Probabilities Empirically and Using Simulation (Optional) 6. Random Variables and Probability Distributions. Random Variables. Probability Distributions for Discrete Random Variables. Probability Distributions for Continuous Random Variables. The Mean and Standard Deviation of a Random Variable. The Normal Distribution. Checking for Normality. The Binomial and Geometric Distributions (Optional). Using the Normal Distribution to Approximate a Discrete Distribution (Optional). Counting Rules, The Poisson Distribution (Online). Section IV: LEARNING FROM SAMPLE DATA. 7. An Overview of Statistical Inference-Learning from Data. Statistical Inference--What We Can Learn From Data. Selecting an Appropriate Method-Four Key Questions. A Five-Step Process for Statistical Inference. 8. Sampling Variability and Sampling Distributions. Statistics and Sampling Variability. The Sampling Distribution of a Sample Proportion. How Sampling Distributions Support Learning From Data. 9. Estimating a Population Proportion. Selecting an Estimator. Estimating a Population Proportion-Margin of Error. A Large-Sample Confidence Interval for a Population Proportion. Choosing a Sample Size to Achieve a Desired Margin of Error. 10. Asking and Answering Questions about a Population Proportion. Hypotheses and Possible Conclusions. Potential Errors in Hypothesis Testing. The Logic of Hypothesis Testing-An Informal Example. A Procedure for Carrying Out a Hypothesis Test. Large-Sample Hypothesis Tests for a Population Proportion. 11. Asking and Answering Questions about the Difference between Two Population Proportions. Estimating the Difference between Two Population Proportions. Testing Hypotheses about the Difference between Two Population Proportions. 12. Asking and Answering Questions about a Population Mean. Sampling Distribution of the Sample Mean. A Confidence Interval for a Population Mean. Testing Hypotheses about a Population Mean. 13. Asking and Answering Questions about the Difference between Two Population Means. Testing Hypotheses about the Difference between Two Population Means Using Independent Samples. Testing Hypotheses about the Difference between Two Population Means Using Paired Samples. Estimating the Difference between Two Population Means. Section V: ADDITIONAL OPPORTUNITIES TO LEARN FROM DATA. 14. Learning from Experiment Data. Variability and Random Assignment. Testing Hypotheses about Differences in Treatment Effects. Estimating a Difference in Treatment Effects. 15. Learning from Categorical Data. Chi-Square Tests for Univariate Categorical Data. Tests for Homogeneity and Independence in a Two-Way Table. 16. Understanding Relationships--Numerical Data Part 2 (Online). The Simple Linear Regression Model. Inferences Concerning the Slope of the Population Regression Line. Checking Model Adequacy. 17. Asking and Answering Questions about More Than Two Means (Online). The Analysis of Variance--SingleFactor ANOVA and the F Test. Multiple Comparisons. Appendix: ANOVA Computations. © 2014, 720pp, Hardback, 9781285049366 www.cengageasia.com 66 PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 8E Jay L. Devore, California Polytechnic State University, San Luis Obispo This market-leading text provides a comprehensive introduction to probability and statistics for engineering students in all specialties. Proven, accurate, and lauded for its excellent examples, PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES evidences Jay Devore’s reputation as an outstanding author and leader in the academic community. Devore emphasizes concepts, models, methodology, and applications as opposed to rigorous mathematical development and derivations. Aided by his lively and realistic examples, students go beyond simply learning about statistics--they also learn how to put statistical methods to use. NEW TO THIS EDITION • More than 40 new examples and 100 new problems were carefully researched and written using the most up-to-date real data. • Chapter 1, “Overview and Descriptive Statistics,” contains a new subsection on “The Scope of Modern Statistics” that describes and exemplifies how statistics is used in modern disciplines. • A significantly revised and simplified Chapter 8, “Tests of Hypotheses Based on a Single Sample,” also has a new subsection entitled “More on Interpreting P-values.” • Wherever possible throughout the book, the language has been tightened and simplified to improve clarity. FEATURES • Sample exams help students build confidence and master concepts prior to taking class exams; the glossary of symbols/acronyms, which includes text page references, is another useful study aid. • Exclusively from Cengage Learning, Enhanced WebAssign® allows you to assign exercises from this text and ensure that your students receive multimedia tutorial support and immediate feedback as they complete their automatically graded assignments. • Several exercises refer to material covered in earlier sections and chapters, allowing students to more easily see the connections between concepts. • Virtually every example and exercise has a real world context. Real data in exercises and examples stimulate students’ interest and enhance their comprehension of concepts. • Notable content includes a strong emphasis on the role that variation plays in statistics, emphasis on the nature of variation in the slope estimate in simple linear regression, and inclusion of a detailed description of pooled t procedures to provide a balance between unpooled and pooled analyses. • “Simulation Experiments” help students gain an understanding of sampling distributions and the insight they provide, especially when a derivation is too complex to carry out. CONTENTS Contents 1. OVERVIEW AND DESCRIPTIVE STATISTICS. Populations, Samples, and Processes. Pictorial and Tabular Methods in Descriptive Statistics. Measures of Location. Measures of Variability. Supplementary Exercises . 2. PROBABILITY. Sample Spaces and Events. Axioms, Interpretations, and Properties of Probability. Counting Techniques. Conditional Probability. Independence. Supplementary Exercises. 3. DISCRETE RANDOM VARIABLES AND PROBABILITY. Random Variables. Probability Distributions for Discrete Random Variables. Expected Values. The Binomial Probability Distribution. Hypergeometric and Negative Binomial Distributions. The Poisson Probability Distribution. Supplementary Exercises. 4. CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS. Probability Density Functions. Cumulative Distribution Functions and Expected Values. The Normal Distribution. The Exponential and Gamma Distributions. Other Continuous Distributions. Probability Plots. Supplementary Exercises. 5. JOINT PROBABILITY DISTRIBUTIONS AND RANDOM SAMPLES. Jointly Distributed Random Variables. Expected Values, Covariance, and Correlation. Statistics and Their Distributions. The Distribution of the Sample Mean. The www.cengageasia.com 67 Distribution of a Linear Combination. Supplementary Exercises. 6. POINT ESTIMATION. Some General Concepts of Point Estimation. Methods of Point Estimation. Supplementary Exercises. 7. STATISTICAL INTERVALS BASED ON A SINGLE SAMPLE. Basic Properties of Confidence Intervals. Large-Sample Confidence Intervals for a Population Mean and Proportion. Intervals Based on a Normal Population Distribution. Confidence Intervals for the Variance and Standard Deviation of a Normal Population. Supplementary Exercises. 8. TESTS OF HYPOTHESES BASED ON A SINGLE SAMPLE. Hypotheses and Test Procedures. Tests About a Population Mean. Tests Concerning a Population Proportion. P-Values. Some Comments on Selecting a Test. Supplementary Exercises. 9. INFERENCES BASED ON TWO SAMPLES. z Tests and Confidence Intervals for a Difference Between Two Population Means. The Two-Sample t Test and Confidence Interval. Analysis of Paired Data. Inferences Concerning a Difference Between Population Proportions. Inferences Concerning Two Population Variances. Supplementary Exercises. 10. THE ANALYSIS OF VARIANCE. Single-Factor ANOVA. Multiple Comparisons in ANOVA. More on Single-Factor ANOVA. Supplementary Exercises. 11. MULTIFACTOR ANALYSIS OF VARIANCE. Two-Factor ANOVA with Kij = 1. Two-Factor ANOVA with Kij = 1. Three-Factor ANOVA. 2p Factorial Experiments. Supplementary Exercises. 12. SIMPLE LINEAR REGRESSION AND CORRELATION. The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter â1. Inferences Concerning µY-x* and the Prediction of Future Y Values. Correlation. Supplementary Exercises. 13. NONLINEAR AND MULTIPLE REGRESSION. Aptness of the Model and Model Checking. Regression with Transformed Variables. Polynomial Regression. Multiple Regression Analysis. Other Issues in Multiple Regression. Supplementary Exercises. 14. GOODNESSOF-FIT TESTS AND CATEGORICAL DATA ANALYSIS. Goodness-of-Fit Tests When Category Probabilities Are Completely Specified. Goodness-of-Fit Tests for Composite Hypotheses. Two-Way Contingency Tables. Supplementary Exercises. 15. DISTRIBUTION-FREE PROCEDURES. The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Distribution-Free Confidence Intervals. Distribution-Free ANOVA. Supplementary Exercises. 16. QUALITY CONTROL METHODS. General Comments on Control Charts. Control Charts for Process Location. Control Charts for Process Variation. Control Charts for Attributes. CUSUM Procedures. Acceptance Sampling. Supplementary Exercises. © 2012, 768pp, Hardback, 9780538733526 PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, INTERNATIONAL EDITION, 8E Jay L. Devore, California Polytechnic State University, San Luis Obispo This market-leading text provides a comprehensive introduction to probability and statistics for engineering students in all specialties. Proven, accurate, and lauded for its excellent examples, PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 8e, International Edition evidences Jay Devore’s reputation as an outstanding author and leader in the academic community. Devore emphasizes concepts, models, methodology, and applications as opposed to rigorous mathematical development and derivations. Aided by his lively and realistic examples, students go beyond simply learning about statistics—they also learn how to put statistical methods to use. NEW TO THIS EDITION • More than 40 new examples and 100 new problems were carefully researched and written using the most up-to-date real data. • Chapter 1, “Overview and Descriptive Statistics,” contains a new subsection on “The Scope of Modern Statistics” that describes and exemplifies how statistics is used in modern disciplines. • A significantly revised and simplified Chapter 8, “Tests of Hypotheses Based on a Single Sample,” also has a new subsection entitled “More on Interpreting P-values.” www.cengageasia.com 68 • Wherever possible throughout the book, the language has been tightened and simplified to improve clarity. FEATURES • Sample exams help students build confidence and master concepts prior to taking class exams; the glossary of symbols/acronyms, which includes text page references, is another useful study aid. • Exclusively from Cengage Learning, Enhanced WebAssign® allows you to assign exercises from this text and ensure that your students receive multimedia tutorial support and immediate feedback as they complete their automatically graded assignments. • Several exercises refer to material covered in earlier sections and chapters, allowing students to more easily see the connections between concepts. • Virtually every example and exercise has a real world context. Real data in exercises and examples stimulate students’ interest and enhance their comprehension of concepts. • Notable content includes a strong emphasis on the role that variation plays in statistics, emphasis on the nature of variation in the slope estimate in simple linear regression, and inclusion of a detailed description of pooled t procedures to provide a balance between unpooled and pooled analyses. • “Simulation Experiments” help students gain an understanding of sampling distributions and the insight they provide, especially when a derivation is too complex to carry out. CONTENTS Contents 1. OVERVIEW AND DESCRIPTIVE STATISTICS. Populations, Samples, and Processes. Pictorial and Tabular Methods in Descriptive Statistics. Measures of Location. Measures of Variability. Supplementary Exercises . 2. PROBABILITY. Sample Spaces and Events. Axioms, Interpretations, and Properties of Probability. Counting Techniques. Conditional Probability. Independence. Supplementary Exercises. 3. DISCRETE RANDOM VARIABLES AND PROBABILITY. Random Variables. Probability Distributions for Discrete Random Variables. Expected Values. The Binomial Probability Distribution. Hypergeometric and Negative Binomial Distributions. The Poisson Probability Distribution. Supplementary Exercises. 4. CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS. Probability Density Functions. Cumulative Distribution Functions and Expected Values. The Normal Distribution. The Exponential and Gamma Distributions. Other Continuous Distributions. Probability Plots. Supplementary Exercises. 5. JOINT PROBABILITY DISTRIBUTIONS AND RANDOM SAMPLES. Jointly Distributed Random Variables. Expected Values, Covariance, and Correlation. Statistics and Their Distributions. The Distribution of the Sample Mean. The Distribution of a Linear Combination. Supplementary Exercises. 6. POINT ESTIMATION. Some General Concepts of Point Estimation. Methods of Point Estimation. Supplementary Exercises. 7. STATISTICAL INTERVALS BASED ON A SINGLE SAMPLE. Basic Properties of Confidence Intervals. Large-Sample Confidence Intervals for a Population Mean and Proportion. Intervals Based on a Normal Population Distribution. Confidence Intervals for the Variance and Standard Deviation of a Normal Population. Supplementary Exercises. 8. TESTS OF HYPOTHESES BASED ON A SINGLE SAMPLE. Hypotheses and Test Procedures. Tests About a Population Mean. Tests Concerning a Population Proportion. P-Values. Some Comments on Selecting a Test. Supplementary Exercises. 9. INFERENCES BASED ON TWO SAMPLES. z Tests and Confidence Intervals for a Difference Between Two Population Means. The Two-Sample t Test and Confidence Interval. Analysis of Paired Data. Inferences Concerning a Difference Between Population Proportions. Inferences Concerning Two Population Variances. Supplementary Exercises. 10. THE ANALYSIS OF VARIANCE. Single-Factor ANOVA. Multiple Comparisons in ANOVA. More on Single-Factor ANOVA. Supplementary Exercises. 11. MULTIFACTOR ANALYSIS OF VARIANCE. Two-Factor ANOVA with Kij = 1. Two-Factor ANOVA with Kij = 1. Three-Factor ANOVA. 2p Factorial Experiments. Supplementary Exercises. 12. SIMPLE LINEAR REGRESSION AND CORRELATION. The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter â1. Inferences Concerning µY-x* and the Prediction of Future Y Values. Correlation. Supplementary Exercises. 13. NONLINEAR AND MULTIPLE REGRESSION. Aptness of the Model and Model Checking. Regression with Transformed Variables. Polynomial Regression. Multiple Regression Analysis. Other Issues in Multiple Regression. Supplementary Exercises. 14. GOODNESSOF-FIT TESTS AND CATEGORICAL DATA ANALYSIS. www.cengageasia.com 69 Goodness-of-Fit Tests When Category Probabilities Are Completely Specified. Goodness-of-Fit Tests for Composite Hypotheses. Two-Way Contingency Tables. Supplementary Exercises. 15. DISTRIBUTION-FREE PROCEDURES. The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Distribution-Free Confidence Intervals. Distribution-Free ANOVA. Supplementary Exercises. 16. QUALITY CONTROL METHODS. General Comments on Control Charts. Control Charts for Process Location. Control Charts for Process Variation. Control Charts for Attributes. CUSUM Procedures. Acceptance Sampling. Supplementary Exercises. © 2012, 736pp, Paperback, 9780840068279 PROBABILITY AND STATISTICS FOR ENGINEERS, 5E Richard L. Scheaffer, University of Florida, Emeritus; Madhuri Mulekar, University of South Alabama; James T. McClave PROBABILITY AND STATISTICS FOR ENGINEERS provides a one-semester, calculus-based introduction to engineering statistics that focuses on making intelligent sense of real engineering data and interpreting results. Traditional topics are presented thorough an accessible modern framework that emphasizes the statistical thinking, data collection and analysis, decision-making, and process improvement skills that engineers need on a daily basis to solve real problems. The text continues to be driven by its hallmark array of engineering applications--thoroughly expanded and modernized for the 5th edition--which tackle timely, interesting, and illuminating scenarios that show students the rich context behind the concepts. Within the presentation of topics and applications the authors continually develop students’ intuition for collecting their own real data, analyzing it with the latest graphical tools, and interpreting the results with a goal of improving quality control and problem-solving process. Students will not only gain solid understanding of concepts and their real-life practicality, but will learn to become active statistical practitioners for their own future careers. NEW TO THIS EDITION • The text’s hallmark applications are thoroughly expanded and modernized with many new problems covering additional engineering situations and more timely, modern issues. New and refreshed problems alike are backed by the most up-to-date available real datasets. • Data collection and analysis is given added emphasis, with more examples and activities given to further develop students’ intuition. A new chapter on obtaining data introduces the common ways of obtaining data in studies, discusses biases introduced by inaccurately applied methods, and shows how technology is used for data collection. • Use of hand calculations has been further scaled back in situations where computations are now more easily accomplished using a computer. Software calculation and analysis is given added emphasis throughout, with updated graphs generated in statistical software to better align with the use of technology and many more visuals overall. • Chapters have been thoroughly reorganized and streamlined to better align with modern courses, improve the overall readability and flow of topics, and provide better consistency and intuitiveness with how concepts introduced later in the book build on concepts introduced earlier. • New hands-on simulation activities have been added providing deeper exploration of key concepts. FEATURES • The text has a user-friendly and less formal empirical approach, consistently stressing quality improvement and real data collection and analysis to develop students’ intuition for actively applying concepts to their careers. • Examples and Exercises strongly highlight real-data engineering applications that capture the full depth and breadth of societal issues that engineers and scientists are called upon to solve. • Graphical techniques such as stemplots, boxplots, and scatterplots are emphasized to build data analysis skills for identifying patterns in data and assessing the www.cengageasia.com 70 underlying assumptions. • Use of statistical software packages is encouraged for easy handling of data when making inferences and building models. Downloadable datasets are available for all real data used in the book in native file formats for the most popular software packages. • All essential traditional engineering statistics topics are covered, and presented concisely and to-thepoint to effectively fit within a one-semester course. Instructors have flexibility to omit optional sections or place added emphasis on some topics to suit different course types and audiences. CONTENTS 1. Data Collection And Exploring Univariate Distributions. 2. Exploring Bivariate Distributions And Estimating Relations 3. Obtaining Data. 4. Probability. 5. Discrete Probability Distributions. 6. Continuous Probability Distributions. 7. Multivariate Probability Distributions. 8. Statistics, Sampling Distributions, And Control Charts. 9. Estimation. 10. Hypothesis Testing. 11. Estimation And Inference For Regression Parameters. 12. Analysis Of Variance. © 2011, 848pp, Hardback, 9780534403027 Coming Soon! PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS, INTERNATIONAL EDITION, 4E NEW TO THIS EDITION • A new Guide of Statistical Methodologies can be used to match statistical inference methodologies to data sets and research questions. It includes examples that are typically used for each methodology. The guide helps students address a common challenge— determining which method or technique is best to use with a particular type of problem. • The Fourth Edition includes more than 200 new and revised problems, with many new data sets and interesting real world problems from every engineering area. Students have raved about the true/ false questions in particular, commenting that they provide a needed self-check opportunity. • A new continuing case study, Internet Marketing, begins before Chapter 1 and runs through Chapter 12. This timely case study, on a topic relevant to students in their daily lives, presents various problems and shows how probability and statistics are used to analyze and solve them. It also helps illustrate the connections among topics within the first 12 chapters. FEATURES Anthony J. Hayter, Georgia Institute of Technology PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS, 4E, International Edition continues the student-oriented approach that has made previous editions successful. As a teacher and researcher at a premier engineering school, author Tony Hayter is in touch with engineers daily—and understands their vocabulary. The result of this familiarity with the professional community is a clear and readable writing style that students understand and appreciate, as well as high-interest, relevant examples and data sets that keep students’ attention. A flexible approach to the use of computer tools, including tips for using various software packages, allows instructors to choose the program that best suits their needs. At the same time, substantial computer output (using MINITAB and other programs) gives students the necessary practice in interpreting output. Extensive use of examples and data sets illustrates the importance of statistical data collection and analysis for students in the fields of aerospace, biochemical, civil, electrical, environmental, industrial, mechanical, and textile engineering, as well as for students in physics, chemistry, computing, biology, management, and mathematics. • Worked examples (77) and more than 150 data sets represent the many different areas of engineering; for instance, civil, mechanical, electrical, industrial, aerospace, biomedical, textile, chemical, and computing. • Dozens of graphs, along with graphical tools, help students learn concepts visually. • To help students grasp concepts, each topic is www.cengageasia.com 71 introduced with references to several real examples from engineering and the sciences. After the topic has been developed technically, a highlighted box reinforces students’ learning by summarizing the important points. • Many examples illustrate proper application of new methodologies, and are developed throughout the chapters as increasingly sophisticated methodologies are considered. This allows students to build on their learning in a manageable way, and understand connections among methodologies. • Computer Note sections offer tips for using various software packages to perform analysis of data sets, which are referenced in the text and available for download from the book’s website. • The applied present ation emphasizes the understanding of underlying concepts and the application of statistical methodologies. • Composition of the book allows flexibility in the order in which the material is taught. The material has been divided into four sections based on probability (Chapters 1-5), basic statistics (Chapters 6-10), advanced statistical methodologies (Chapters 11-14), and additional topics (Chapters 15-17). The Preface offers suggested paths that instructors may follow based on topic preference, making the book ideal for departments in which different methods of teaching coexist. CONTENTS 1. PROBABILITY THEORY. Probabilities. Events. Combinations of Events. Conditional Probability. Probabilities of Event Intersections. Posterior Probabilities. Counting Techniques. 2. RANDOM VARIABLES. Discrete Random Variables. Continuous Random Variables. The Expectation of a Random Variable. The Variance of a Random Variable. Jointly Distributed Random Variables. Combinations and Functions of Random Variables. 3. DISCRETE PROBABILIT Y DISTRIBUTIONS. The Binomial Distribution. The Geometric and Negative Binomial Distributions. The Hypergeometric Distribution. The Poisson Distribution. The Multinomial Distribution. 4. CONTINUOUS PROBABILITY DISTRIBUTIONS. The Uniform Distribution. The Exponential Distribution. The Gamma Distribution. The Weibull Distribution. The Beta Distribution. 5. THE NORMAL DISTRIBUTION. Probability Calculations Using the Normal Distribution. Linear Combinations of Normal Random Variables. Approximating Distributions with the Normal Distribution. Distributions Related to the Normal Distribution. 6. DESCRIPTIVE STATISTICS. Experimentation. Data Presentation. Sample Statistics. Examples. 7. STATISTICAL ESTIMATION AND SAMPLING DISTRIBUTIONS. Point Estimates. Properties of Point Estimates. Sampling Distributions. Constructing Parameter Estimates. 8. INFERENCES ON A POPULATION MEAN. Confidence Intervals. Hypothesis Testing. Summary. 9. COMPARING TWO POPULATION MEANS. Introduction. Analysis of Paired Samples. Analysis of Independent Samples. Summary. 10. DISCRETE DATA ANALYSIS. Inferences on a Population Proportion. Comparing Two Population Proportions. Goodness-of-Fit Tests for One-Way Contingency Tables. Testing for Independence in Two-Way Contingency Tables. 11. THE ANALYSIS OF VARIANCE. One Factor Analysis of Variance. Randomized Block Designs. 12. SIMPLE LINEAR REGRESSION AND CORRELATION. The Simple Linear Regression Model. Fitting the Regression Line. Inferences on the Slope Parameter ß1. Inferences on the Regression Line. Prediction Intervals for Future Response Values. The Analysis of Variance Tables. Residual Analysis. Variable Transformation. Correlation Analysis. 13. MULTIPLE LINEAR REGRESSION AND NONLINEAR REGRESSION. Introduction to Multiple Linear Regression. Examples of Multiple Linear Regression. Matrix Algebra Formulation of Multiple Linear Regression. Evaluating Model Adequacy. Nonlinear Regression. 14. MULTIFACTOR EXPERIMENTAL DESIGN AND ANALYSIS. Experiments with Two Factors. Experiments with Three or More Factors. 15. NONPARAMETRIC STATISTICAL ANALYSIS. The Analysis of a Single Population. Comparing Two Populations. Comparing Three or More Populations. 16. QUALITY CONTROL METHODS. Introduction. Statistical Process Control. Variable Control Charts. Attribute Control Charts. Acceptance Sampling. 17. RELIABILITY ANALYSIS AND LIFE TESTING. System Reliability. Modeling Failure Rates. Life Testing. Tables. Answers to Odd-Numbered Problems. Index. © 2013, 864pp, Paperback, 9781133112143 www.cengageasia.com 72 PROBABILITY AND STATISTICS FOR ENGINEERS, INTERNATIONAL EDITION, 5E Richard L. Scheaffer, University of Florida, Emeritus; Madhuri Mulekar, University of South Alabama; James T. McClave PROBABILITY AND STATISTICS FOR ENGINEERS, 5e, International Edition provides a one-semester, calculus-based introduction to engineering statistics that focuses on making intelligent sense of real engineering data and interpreting results. Traditional topics are presented thorough an accessible modern framework that emphasizes the statistical thinking, data collection and analysis, decision-making, and process improvement skills that engineers need on a daily basis to solve real problems. The text continues to be driven by its hallmark array of engineering applications—thoroughly expanded and modernized for the 5th edition—which tackle timely, interesting, and illuminating scenarios that show students the rich context behind the concepts. Within the presentation of topics and applications the authors continually develop students’ intuition for collecting their own real data, analyzing it with the latest graphical tools, and interpreting the results with a goal of improving quality control and problem-solving process. Students will not only gain solid understanding of concepts and their real-life practicality, but will learn to become active statistical practitioners for their own future careers. NEW TO THIS EDITION • The text’s hallmark applications are thoroughly expanded and modernized with many new problems covering additional engineering situations and more timely, modern issues. New and refreshed problems alike are backed by the most up-to-date available real datasets. • Data collection and analysis is given added emphasis, with more examples and activities given to further develop students’ intuition. A new chapter on obtaining data introduces the common ways of obtaining data in studies, discusses biases introduced by inaccurately applied methods, and shows how technology is used for data collection. • Use of hand calculations has been further scaled back in situations where computations are now more easily accomplished using a computer. Software calculation and analysis is given added emphasis throughout, with updated graphs generated in statistical software to better align with the use of technology and many more visuals overall. • Chapters have been thoroughly reorganized and streamlined to better align with modern courses, improve the overall readability and flow of topics, and provide better consistency and intuitiveness with how concepts introduced later in the book build on concepts introduced earlier. • New hands-on simulation activities have been added providing deeper exploration of key concepts. FEATURES • The text has a user-friendly and less formal empirical approach, consistently stressing quality improvement and real data collection and analysis to develop students’ intuition for actively applying concepts to their careers. • Examples and Exercises strongly highlight real-data engineering applications that capture the full depth and breadth of societal issues that engineers and scientists are called upon to solve. • Graphical techniques such as stemplots, boxplots, and scatterplots are emphasized to build data analysis skills for identifying patterns in data and assessing the underlying assumptions. • Use of statistical software packages is encouraged for easy handling of data when making inferences and building models. Downloadable datasets are available for all real data used in the book in native file formats for the most popular software packages. • All essential traditional engineering statistics topics are covered, and presented concisely and to-thepoint to effectively fit within a one-semester course. Instructors have flexibility to omit optional sections or place added emphasis on some topics to suit different course types and audiences. www.cengageasia.com 73 CONTENTS 1. DATA COLLECTION AND EXPLORING UNIVARIATE DISTRIBUTIONS Introduction. A model for problem solving and its application. Types of data and frequency distribution tables. Tools for describing data: Graphical methods. Graphing Categorical Data. Graphing Numerical Data. Visualizing distributions. Tool for Describing Data: Numerical measures. Measures of Center. Measures of Position. Measures of variation (or spread). Reading Computer Printouts. The effect of shifting and scaling of measurements on summary measures. Summary Measures and Decisions. The Empirical Rule. Standardized Values and z-scores. Boxplots. Detecting Outliers. Summary. Supplemental Exercises. 2. EXPLORING BIVARIATE DISTRIBUTIONS AND ESTIMATING RELATIONS Introduction. Twoway table for categorical data. Time series analysis. Scatterplots: Graphical analysis of association between measurements. Correlation: Estimating the strength of linear relation. Regression: Modeling linear relationships. The Coefficient of Determination. Residual Analysis: Assessing the adequacy of the model. Transformations. Reading Computer Printout. Summary. Supplemental Exercises. 3. OBTAINING DATA. Introduction. Overview of methods of data collection. Planning and Conducting Surveys. Planning and Conducting Experiments. Completely Randomized Design. Randomized Block Design. Planning and Conducting an Observational Study. Summary. Supplemental Exercises. 4. PROBABILITY. Introduction. Sample space and relationships among events. Definition of probability. Counting rules useful in probability. Conditional probability and independence. Rules of probability. Odds, odds ratios, and risk ratio. Summary. Supplemental Exercises. 5. DISCRETE PROBABILITY DISTRIBUTIONS. Introduction. Random variables and their probability distributions Expected values of random variables. The Bernoulli distribution. The Binomial distribution. The Geometric and Negative Binomial distributions. The Geometric distribution. The Negative Binomial distribution. The Poisson distribution. The hypergeometric distribution. The Moment-Generating Function. Simulating probability distributions. Summary. Supplementary Exercises. 6. CONTINUOUS PROBABILITY DISTRIBUTIONS. Introduction. Continuous random variables and their probability distributions. Expected values of continuous random variables. The Uniform distribution. The exponential distribution. The Gamma distribution. The Normal distribution. The Lognormal Distribution. The Beta distribution. The Weibull distribution. Reliability. The Moment-generating Functions for Continuous Random Variables. Simulating probability distributions. Summary. Supplementary Exercises. 7. MULTIVARIATE PROBABILITY DISTRIBUTIONS. Introduction. Bivariate and Marginal Probability Distributions. Conditional Probability Distributions. Independent Random Variables. Expected Values of Functions of Random Variables. The Multinomial Distribution. More on the Moment-Generating Function. Conditional Expectations. Compounding and Its Applications. Summary. Supplementary Exercises. 8. STATISTICS, SAMPLING DISTRIBUTIONS, AND CONTROL CHARTS. Introduction. The sampling distributions. The sampling distribution of X (General Distribution). The sampling distribution of X (Normal Distribution). The sampling distribution of sample proportion Y/n (Large sample). The sampling distribution of S² (Normal Distribution). Sampling Distributions: The multiple-sample case. The sampling distribution of (X1 – X2). The sampling distribution of XD. The sampling distribution of (^p1 – ^p2). The sampling distribution of S²1/S²2. Control Charts. The X-Chart: Known μ and σ. The X and R-Charts: Unknown μ and σ. The X and S-Charts: Unknown μ and σ. The p-Chart. The c-chart. The u-chart. Process Capability. Summary. Supplementary Exercises. 9. ESTIMATION. Introduction. Point estimators and their properties. Confidence Intervals: The Single-Sample Case. Confidence Interval for μ: General Distribution. Confidence Interval for Mean: Normal Distribution. Confidence Interval for Proportion: Large sample case. Confidence interval for σ². Confidence Intervals: The Multiple Samples Case. Confidence Interval for Linear Functions of Means: General Distributions. Confidence Interval for Linear Functions of Means: Normal Distributions. Large Samples Confidence Intervals for Linear Functions of Proportions. Confidence Interval for σ²2/σ²1: Normal distribution case. Prediction Intervals. Tolerance Intervals. The Method of Maximum Likelihood. Bayes Estimators. Summary. Supplementary Exercises. 10. HYPOTHESIS TESTING. Introduction. Terminology of Hypothesis Testing. Hypothesis Testing: The SingleSample Case. Testing for Mean: General Distributions Case. Testing a Mean: Normal distribution Case. Testing www.cengageasia.com 74 for Proportion: Large Sample Case. Testing for Variance: Normal Distribution Case. Hypothesis Testing: The Multiple-Sample Case. Testing the Difference between Two means: General Distributions Case. Testing the Difference between Two means: Normal Distributions case. Testing the difference between the means for paired samples. Testing the ratio of variances: Normal distributions case. χ² tests on Frequency data. Testing parameters of the multinomial distribution. Testing equality among Binomial parameters. Test of Independence. Goodness of Fit Tests. χ² Test. Kolmogorov-Smirnov test. Using Computer Programs to Fit Distributions. Acceptance Sampling. Acceptance Sampling by Attributes. Acceptance Sampling by Variables. Summary. Supplementary Exercises. 11. ESTIMATION AND INFERENCE FOR REGRESSION PARAMETERS. Introduction. Regression models with one predictor variable. The probability distribution of random error component. Making inferences about slope. Estimating slope using a confidence interval. Testing a hypothesis about slope. Connection between inference for slope and correlation coefficient. Using the simple linear model for estimation and prediction. Multiple regression analysis. Fitting the model: The least-squares approach. Estimation of error variance. Inferences in multiple regression. A test of model adequacy. Estimating and testing hypothesis about individual parameters. Using the multiple regression model for estimation and prediction. Model building: A test for portion of a model. Other regression models. Response surface method. Modeling a time trend. Logistic regression. Checking conditions and some pitfalls. Checking conditions. Some pitfalls. Reading printouts. Summary. Supplemental Exercises. 12. ANALYSIS OF VARIANCE. Introduction. Review of Designed Experiments. Analysis of Variance (ANOVA) Technique. Analysis of Variance for Completely Randomized Design. Relationship of ANOVA for CRD with a t test and Regression. Equivalence between a t test and an F test for CRD with 2 treatments. ANOVA for CRD and Regression Analysis. Estimation for Completely randomized design. Analysis of Variance for the Randomized Block Design. ANOVA for RBD. Relation between a Paired t test and an F test for RBD. ANOVA for RBD and Regression Analysis. Bonferroni Method for Estimation for RBD. Factorial Experiments. Analysis of variance for the Factorial Experiment. Fitting Higher Order Models. Summary. Supplemental Exercises. APPENDIX. REFERENCES. © 2011, 848pp, Paperback, 9780538735902 PROBABILITY FOR ENGINEERING, MATHEMATICS, AND SCIENCE, INTERNATIONAL EDITION Chris P. Tsokos, University of South Florida This text blends theory and applications, reinforcing concepts with practical real-world examples that illustrate the importance of probability to undergraduate students who will use it in their subsequent courses and careers. The author emphasizes the study of probability distributions that characterize random variables, because this knowledge is essential in performing parametric statistical analysis. Explanations include the “why” as well as the “how” of probability distributions for random variables to help engage students and further promote their understanding. In addition, the text includes a self-contained chapter on finite Markov chains, which introduces the basic aspects of Markov chains and illustrates their usefulness with several real examples. FEATURES • This text offers a clear and illustrative presentation of the fundamental aspects of probability, focusing on both its meaning and its usefulness. • Throughout the text, topics are developed using a step-by-step approach and clarified by graphical illustrations. • New concepts are illustrated and interpreted both graphically and analytically, addressing students’ different learning styles and promoting their understanding by showing them that a problem can be solved in multiple ways. www.cengageasia.com 75 • Boxes highlight important concepts, results, and the probability distributions, assisting students in study and review. • The author places special emphasis on the development of the classical discrete and continuous probability distributions, their properties, and their interdisciplinary relevance to real life problems in our global society. • The numerous exercises include items that test theoretical understanding as well as applications drawn from several areas in engineering, mathematics, and the sciences. • A self-contained chapter on discrete finite Markov chains includes several applications that demonstrate their relevance as well as step-by-step development of the theory, clearly illustrated by state and tree diagrams. • An extensive review of necessary mathematics that may not be covered in a basic calculus course—such as set theory, computational methods, binomial and multinomial theorems, matrices, Jacobians, and gamma and beta functions—is presented in an appendix for the convenience of the student. CONTENTS 1. PROBABILITY. Definition of Probability. Axiomatic Definition of Probability. Conditional Probability. Marginal Probabilities. Bayes’ Theorem. Independent Events. Combinatorial Probability. 2. DISCRETE PROBABILITY DISTRIBUTIONS. Discrete Probability Density Function. Cumulative Distribution Function. The Point Binomial Distribution. The Binomial Probability Distribution. The Poisson Probability Distribution. The Hypergeometric Probability Distribution. The Geometric Probability Distribution. The Negative Binomial Probability Distribution. 3. PROBABILITY DISTRIBUTIONS OF CONTINUOUS RANDOM VARIABLES. Continuous Random Variable and Probability Density Function. Cumulative Distribution Function of a Continuous Random Variable. The Continuous Probability Distributions. 4. FUNCTIONS OF A RANDOM VARIABLE. Introduction. Distribution of a Continuous Function of a Discrete Random Variable. Distribution of a Continuous Function of a Continuous Random Variable. Other Types of Derived Distributions. 5. EXPECTED VALUES, MOMENTS AND MOMENT GENERATING FUNCTIONS. Mathematical Expectation. Properties of Expectation. Moments. Moment Generating Function. 6. TWO RANDOM VARIABLES. Joint Probability Density Function. Bivariate Cumulative Distribution Function. Marginal Probability Distributions. Conditional Probability Density and Cumulative Distribution Functions. Independent Random Variables. Function of Two Random Variables. Expected Value and Moments. Conditional Expectation. Bivariate Normal Distribution. 7. SEQUENCE OF RANDOM VARIABLES. Multivariate Probability Density Functions. Multivariate Cumulative Distribution Functions. Marginal Probability Distributions. Conditional Probability Density and Cumulative Distribution Functions. Sequence of Independent Random Variables. Functions of Random Variables. Expected Value and Moments. Conditional Expectation. 8. LIMIT THEOREMS. Chebyshev’s Inequality. Bernoulli’s Law of Large Numbers. Weak and Strong Laws of Large Numbers. The Central Limit Theorem. The DeMoivreLaplace Theorem. Normal Approximation to the Poisson Distribution. Normal Approximation to the Gamma Distribution. 9. FINITE MARKOV CHAINS. Basic Concepts. N-Step Transitions Problems. Evaluation of Pn. Classification of States. Appendix. Index. © 2012, 608pp, Paperback, 9781111580735 PROBABILITY FOR ENGINEERING, MATHEMATICS, AND SCIENCES Chris P. Tsokos, University of South Florida This text blends theory and applications, reinforcing concepts with practical real-world examples that illustrate the importance of probability to undergraduate students who will use it in their subsequent courses and careers. The author emphasizes the study of probability distributions that characterize random variables, because this knowledge is essential in performing www.cengageasia.com 76 parametric statistical analysis. Explanations include the “why” as well as the “how” of probability distributions for random variables to help engage students and further promote their understanding. In addition, the text includes a self-contained chapter on finite Markov chains, which introduces the basic aspects of Markov chains and illustrates their usefulness with several real examples. FEATURES • This text offers a clear and illustrative presentation of the fundamental aspects of probability, focusing on both its meaning and its usefulness. • Throughout the text, topics are developed using a step-by-step approach and clarified by graphical illustrations. • New concepts are illustrated and interpreted both graphically and analytically, addressing students’ different learning styles and promoting their understanding by showing them that a problem can be solved in multiple ways. • Boxes highlight important concepts, results, and the probability distributions, assisting students in study and review. • The author places special emphasis on the development of the classical discrete and continuous probability distributions, their properties, and their interdisciplinary relevance to real life problems in our global society. • The numerous exercises include items that test theoretical understanding as well as applications drawn from several areas in engineering, mathematics, and the sciences. • A self-contained chapter on discrete finite Markov chains includes several applications that demonstrate their relevance as well as step-by-step development of the theory, clearly illustrated by state and tree diagrams. • An extensive review of necessary mathematics that may not be covered in a basic calculus course—such as set theory, computational methods, binomial and multinomial theorems, matrices, Jacobians, and gamma and beta functions—is presented in an appendix for the convenience of the student. CONTENTS Marginal Probabilities. Bayes’ Theorem. Independent Events. Combinatorial Probability. 2. DISCRETE PROBABILITY DISTRIBUTIONS. Discrete Probability Density Function. Cumulative Distribution Function. The Point Binomial Distribution. The Binomial Probability Distribution. The Poisson Probability Distribution. The Hypergeometric Probability Distribution. The Geometric Probability Distribution. The Negative Binomial Probability Distribution. 3. PROBABILITY DISTRIBUTIONS OF CONTINUOUS RANDOM VARIABLES. Continuous Random Variable and Probability Density Function. Cumulative Distribution Function of a Continuous Random Variable. The Continuous Probability Distributions. 4. FUNCTIONS OF A RANDOM VARIABLE. Introduction. Distribution of a Continuous Function of a Discrete Random Variable. Distribution of a Continuous Function of a Continuous Random Variable. Other Types of Derived Distributions. 5. EXPECTED VALUES, MOMENTS AND MOMENT GENERATING FUNCTIONS. Mathematical Expectation. Properties of Expectation. Moments. Moment Generating Function. 6. TWO RANDOM VARIABLES. Joint Probability Density Function. Bivariate Cumulative Distribution Function. Marginal Probability Distributions. Conditional Probability Density and Cumulative Distribution Functions. Independent Random Variables. Function of Two Random Variables. Expected Value and Moments. Conditional Expectation. Bivariate Normal Distribution. 7. SEQUENCE OF RANDOM VARIABLES. Multivariate Probability Density Functions. Multivariate Cumulative Distribution Functions. Marginal Probability Distributions. Conditional Probability Density and Cumulative Distribution Functions. Sequence of Independent Random Variables. Functions of Random Variables. Expected Value and Moments. Conditional Expectation. 8. LIMIT THEOREMS. Chebyshev’s Inequality. Bernoulli’s Law of Large Numbers. Weak and Strong Laws of Large Numbers. The Central Limit Theorem. The DeMoivreLaplace Theorem. Normal Approximation to the Poisson Distribution. Normal Approximation to the Gamma Distribution. 9. FINITE MARKOV CHAINS. Basic Concepts. N-Step Transitions Problems. Evaluation of Pn. Classification of States. Appendix. Index. © 2012, 608pp, Hardback, 9781111430276 1. PROBABILITY. Definition of Probability. Axiomatic Definition of Probability. Conditional Probability. www.cengageasia.com 77 SEEING THROUGH STATISTICS, 4E Jessica M. Utts, University of California, Irvine This fourth edition of this popular book by Jessica Utts develops statistical literacy and critical thinking through real-world applications, with an emphasis on ideas, not calculations. This text focuses on the key concepts that educated citizens need to know about statistics. These ideas are introduced in interesting applied and real contexts, without using an abundance of technicalities and calculations that only serve to confuse students. NEW TO THIS EDITION • InfoTrac® Student Collections are specialized databases expertly drawn from the Gale Academic One library. Each InfoTrac® Student Collection enhances the student learning experience in the specific course area related to the product. These specialized databases allow access to hundreds of scholarly and popular publications - all reliable sources - including journals, encyclopedias, and academic reports. Learn more and access at: http://gocengage. com/infotrac. • The book includes many updated examples and new case studies. • There is a new chapter on simulating probabilities. • The chapter on Time Series has been split up and moved into the chapter on graphs and the chapter on Economic News. • The chapter previously called Reading the Economic News has been changed to “Understanding Uncertainty in Economics” and moved to the end of Part 3. • The wording has been streamlined throughout. FEATURES • There is a new Section at the end of each chapter called “Thinking about Key Concepts” that “closes the loop” on the Thought Questions presented at the beginning of that chapter. • A “Guide for Educated Citizens” theme is woven throughout the book to increase student’s interest in text. This theme underscores the importance of statistical literacy to everyday life. This includes understanding the difference between statistical significance and practical importance, the idea that coincidences and improbable events are not collectively uncommon, and other frequently misunderstood statistical concepts. • Java applets have been included on the companion website that accompanies the text. The applets give students more opportunity for hands-on learning and allow students to explore statistics on their own. CONTENTS PART 1: FINDING DATA IN LIFE. 1. THE BENEFITS AND RISKS OF USING STATISTICS 1.1 Statistics. Case study. 1.1 Heart or Hypothalamus? 1.2 Detecting Patterns and Relationships. Case study. 1.2 Does Aspirin Prevent Heart Attacks? 1.3 Don’t Be Deceived by Improper Use of Statistics. CASE STUDY 1.3 [To come]. 1.4 Summary and Conclusions. Thinking About Key Concepts. Exercises. Mini-Projects. References. 2. READING THE NEWS. 2.1 The Educated Consumer of Data. 2.2 Origins of News Stories. 2.3 How to be a Statistics Sleuth: Seven Critical Components. 2.4 Four Hypothetical Examples of Bad Reports. CASE STUDY 2.1 Who Suffers from Hangovers? 2.5 Planning Your Own Study: Defining the Components in Advance. CASE STUDY 2.2. Thinking About Key Concepts. Exercises. Mini-Projects. References. 3. MEASUREMENTS, MISTAKES, AND MISUNDERSTANDINGS. 3.1 Simple Measures Don’t Exist. 3.2 It’s All in the Wording. CASE STUDY 3.1 No Opinion of Your Own? Let Politics Decide. 3.3 Open or Closed Questions: Should Choices Be Given?. 3.4 Defining What Is Being Measured. CASE STUDY 3.2. 3.5 Defining a Common Language. Thinking About Key Concepts. Exercises. Mini-Projects. References. 4. HOW TO GET A GOOD SAMPLE. 4.1 Common Research Strategies. 4.2 Defining a Common Language. 4.3 The Beauty of Sampling. 4.4 Simple Random Sampling. 4.5 Other Sampling Methods. 4.6 Difficulties and Disasters in Sampling. CASE STUDY 4.1 The Infamous Literary Digest Poll of 1936. Thinking About Key Concepts. Exercises. Mini-Projects. References. 5. EXPERIMENTS www.cengageasia.com 78 AND OBSERVATIONAL STUDIES. 5.1 Defining a Common Language. 5.2 Designing a Good Experiment. CASE STUDY 5.1 Quitting Smoking with Nicotine Patches. 5.3 Difficulties and Disasters in Experiments. CASE STUDY 5.2 Beat the Heat with a Frozen Treat. 5.4 Designing a Good Observational Experiment. CASE STUDY 5.3. 5.5 Difficulties and Disasters in Observational Studies. 5.6 Random Sample versus Random Assignment. Thinking About Key Concepts. Exercises. Mini-Projects. References 6. GETTING THE BIG PICTURE. 6.1 Final Questions. CASE STUDY 6.1 Mozart, Relaxation, and Performance on Spatial Tasks. CASE STUDY. 6.2 Can Meditation Boost Test Scores?. CASE STUDY 6.3 Drinking, Driving, and the Supreme Court. CASE STUDY 6.4 Smoking During Pregnancy and Child’s IQ. CASE STUDY 6.5 For Class Discussion: Guns and. Homicides at Home. Mini-Projects. References. PART II: FINDING LIFE IN DATA. 7. SUMMARIZING AND DISPLAYING MEASUREMENT DATA. 7.1 Turning Data into Information. 7.2 Picturing Data: Stemplots and Histograms. 7.3 Five Useful Numbers: A Summary. 7.4 Boxplots. 7.5 Traditional Measures: Mean, Variance, and Standard Deviation. 7.6 Caution: Being Average Isn’t Normal. CASE STUDY 7.1 Detecting Exam Cheating with a Histogram. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References. 8. BELL-SHAPED CURVES AND OTHER SHAPES. 8.1 Populations, Frequency Curves, and Proportions. 8.2 The Pervasiveness of Normal Curves. 8.3 Percentiles and Standardized Scores. 8.4 z-Scores and Familiar Intervals. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. References 9. PLOTS, GRAPHS, AND PICTURES. 9.1 Well-Designed Statistical Pictures. 9.2 Pictures of Categorical Data. 9.3 Pictures of Measurement Variables. 9.4 Pictures Trends across Time. 9.5 Difficulties and Disasters in Plots, Graphs, and Pictures. 9.6 A Checklist for Statistical Pictures. CASE STUDY 9.1. Thinking About Key Concepts. Exercises. Mini-Projects. References. 10. RELATIONSHIPS BETWEEN MEASUREMENT VARIABLES. 10.1 Statistical Relationships. 10.2 Strength versus Statistical Significance. 10.3 Measuring Strength Through Correlation. 10.4 Specifying Linear Relationships with Regression. CASE STUDY 10.1 Are Attitudes about Love and Romance Hereditary? CASE STUDY 10.2 A Weighty Issue: Women Want Less, Men Want More. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References. 11. RELATIONSHIPS CAN BE DECEIVING. 11.1 Illegitimate Correlations. 11.2 Legitimate Correlation Does Not Imply Causation. 11.3 Some Reasons for Relationships Between Variables. 11.4 Confirming Causation. Case Study 11.1. Thinking About Key Concepts. Exercises. Mini-Projects. References. 12. RELATIONSHIPS BETWEEN CATEGORICAL VARIABLES 12.1 Displaying Relationships Between Categorical Variables: Contingency Tables. 12.2 Relative Risk, Increased Risk, and Odds. 12.3 Misleading Statistics about Risk. 12.4 Simpson’s Paradox: The Missing Third Variable. CASE STUDY 12.1 Assessing Discrimination in Hiring and Firing. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References. CH 13. STATISTICAL SIGNIFICANCE FOR 2X2 TABLES 13.1 Measuring the Strength of the Relationship. 13.2 Steps for Assessing Statistical Significance. 13.3 The Chi-Square Test. 13.4 Practical versus Statistical Significance. CASE STUDY 13.1 Extrasensory Perception Works Best with Movies. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References. PART III: UNDERSTANDING UNCERTAINTY IN LIFE. 14. UNDERSTANDING PROBABILITY AND LONG-TERM EXPECTATIONS 14.1 Probability. 14.2 The RelativeFrequency Interpretation. 14.3 The Personal-Probability Interpretation. 14.4 Applying Some Simple Probability Rules. 14.5 When Will It Happen? 14.6 Long-Term Gains, Losses, and Expectations. CASE STUDY 14.1 Birthdays and Death Days—Is There a Connection? Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References. 15. UNDERSTANDING UNCERTAINTY THROUGH SIMULATION 15.1 Mimicking Reality through Simulation. 15.2 Simulating the ChiSquare Test. 15.3 Randomization Tests. 15.4 Simulating Probabilities. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References. 16. PSYCHOLOGICAL INFLUENCES ON PERSONAL PROBABILITY 16.1 Revisiting Personal Probability. 16.2 Equivalent Probabilities; Different Decisions. 16.3 How Personal Probabilities can Be Distorted. 16.4 Optimism, Reluctance to Change, and Overconfidence. 16.5 Calibrating Personal Probabilities of Experts. CASE STUDY 16.1 Calibrating Weather Forecasters and Physicians. 16.6 Tips for Improving Your Personal Probabilities and Judgments. Thinking About Key. Concepts. Exercises. Mini-Projects. References. 17. www.cengageasia.com 79 WHEN INTUITION DIFFERS FROM RELATIVE FREQUENCY 17.1 Revisiting Relative Frequency. 17.2 Coincidences. 17.3 The Gambler’s Fallacy. 17.4 Confusion of the Inverse. CASE STUDY 17.1 Streak Shooting in Basketball: Reality or Illusion?. 17.5 Using Expected Values to Make Wise Decisions. CASE STUDY 17.2. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References. 18. UNDERSTANDING UNCERTAINTY IN ECONOMICS. 18.1 Cost of Living: The Consumer Price Index. 18.2 Uses of the Consumer Price Index. 18.3 Criticisms of the Consumer Price Index. 18.4 Seasonal Adjustments: Reporting the Consumer Price Index 18.5 Economic Indicators. CASE STUDY 18.1 Did Wages Really Go Up in the Reagan–Bush Years? Thinking About Key Concepts. Exercises. Mini-Projects. References. PART IV: MAKING JUDGMENTS FROM SURVEYS AND EXPERIMENTS. 19. THE DIVERSITY OF SAMPLES FROM THE SAME POPULATION. 19.1 Setting the Stage. 19.2 What to Expect of Sample Proportions. 19.3 What to Expect of Sample Means. 19.4 What to Expect in Other Situations. CASE STUDY 19.1. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References. 20. ESTIMATING PROPORTIONS WITH CONFIDENCE. 20.1 Confidence Intervals. 20.2 Three Examples of Confidence Intervals from the Media. 20.3 Constructing a Confidence Interval for a Proportion. CASE STUDY 20.1. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References. CH 21. THE ROLE OF CONFIDENCE INTERVALS IN RESEARCH. 21.1 Confidence Intervals for Population Means. 21.2 Confidence Intervals for the Difference Between Two Means. 21.3 Revisiting Case Studies: How Journals Present Confidence Intervals. 21.4 Understanding Any Confidence Interval. CASE STUDY 21.1 Premenstrual Syndrome? Try Calcium. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References. 22. REJECTING CHANCE—TESTING HYPOTHESES IN RESEARCH. 22.1 Using Data to Make Decisions. 22.2 The Basic Steps for Testing Hypotheses. 22.3 Testing Hypotheses for Proportions. 22.4 What Can Go Wrong: The Two Types of Errors. CASE STUDY 22.1 Testing for the Existence of Extrasensory Perception. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References. 23. HYPOTHESIS TESTING—EXAMPLES AND CASE STUDIES. 23.1 How Hypothesis Tests are Reported in the News. 23.2 Testing Hypotheses about Proportions and Means. 23. 3 Revisiting Case Studies: How Journals Present Hypothesis Tests. CASE STUDY 23.1 An Interpretation of a p-Value Not Fit to Print. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References. 24. SIGNIFICANCE, IMPORTANCE, AND UNDETECTED DIFFERENCES. 24.1 Real Importance versus Statistical Significance. 24.2 The Role of Sample Size in Statistical Significance. 24.3 No Difference versus No Statistically Significant Difference. CASE STUDY 24.1 Does Eating Breakfast Cereal Produce More Boys? 24.4 Multiple Tests and False Positives. 24.5 A Summary of Warnings. CASE STUDY 24.2. Thinking About Key Concepts. Exercises. Mini-Projects. References. 25. METAANALYSIS: RESOLVING INCONSISTENCIES ACROSS STUDIES. 25.1 The Need for Meta-Analysis. 25.2 Two Important Decisions for the Analyst. CASE STUDY 25.1 Smoking and Reduced Fertility. 25.3 Some Benefits of Meta-Analysis. 25.4 Criticisms of Meta-Analysis. CASE STUDY 25.2 Controversy over Breast Cancer Screening. Thinking About Key Concepts. Exercises. Mini-Projects. References. 26. ETHICS IN STATISTICAL STUDIES. 26.1 Ethical Treatment of Human and Animal Participants. 26.2 Assurance of Data Quality. 26.3 Appropriate Statistical Analyses. 26.4 Fair Reporting of Results. CASE STUDY 26.1 Science Fair Project or Fair Science Project? Exercises. References. 27. PUTTING WHAT YOU HAVE LEARNED TO THE TEST. © 2015, 656pp, Paperback, 9781285050881 www.cengageasia.com 80 STAT 2 (WITH REVIEW CARDS AND COURSEMATE PRINTED ACCESS CARD), 2E Robert R. Johnson, Monroe Community College; Patricia J. Kuby Created through a “student-tested, faculty-approved” review process, STAT 2 is an engaging and accessible solution to accommodate the diverse lifestyles of today’s learners at a value-based price. This concise STAT 2 includes all of the key concepts that you require and a full suite of learning aids--including chapter-bychapter self-quizzes and downloadable flash cards--and a wealth of online teaching and learning resources. NEW TO THIS EDITION • Many new real-world examples that students can relate to have been added, such as Teens using Cell Phones in Class, Busy Behind the Wheel, What Employers Look for in Seasonal Employees, and Polishing a Microchip. • New problems have been added at the end of each text chapter, as well as online. • Updated content throughout includes the use of ogives in Chapter 2 and a new method of finding area in Chapter 9. • New Key Terms have been added to many of the chapters. Designed to be easy to spot, these key terms come in handy when students are reviewing the material or preparing for quizzes. • Some of the sections have been reorganized, improving clarity and concept sequencing. • Chapter 4 includes new equations of properties of probability numbers, and an expanded Notes section in the discussion of probability numbers. • Chapter 6 includes a new section on common z values.Specifically, the chapter has been completely rewritten to present the Standard Normal Distribution utilizing the cumulative area approach, incorporating a more intuitive idea with respect to the total area under a curve and following more closely the format used with calculators and statistical software. • Chapter 7 includes a new discussion about the census. • Interested in a simple way to complement your text and course content with study and practice materials? Cengage Learning’s CourseMate brings course concepts to life with interactive learning, study, and exam preparation tools that support the printed textbook. Watch student comprehension soar as your class works with the printed textbook and the textbook-specific website. • Available with this text, Aplia™ is an online interactive learning solution that improves comprehension and outcomes by increasing student effort and engagement. Founded by a professor to enhance his own courses, Aplia provides automatically graded assignments with detailed, immediate explanations on every question, and innovative teaching materials. Our easy-to-use system has been used by more than 1,000,000 students at over 1800 institutions. CONTENTS Part I: DESCRIPTIVE STATISTICS. 1. Statistics. 2. Descriptive Analysis and Presentation of Single-Variable Data. 3. Descriptive Analysis and Presentation of Bivariate Data. Part II: PROBABILITY. 4. Probability. 5. Probability Distributions (Discrete Variables). 6. Normal Probability Distributions. 7. Sample Variability. Part III: INFERENTIAL STATISTICS. 8. Introduction to Statistical Inferences. 9. Inferences Involving One Population. 10. Inferences Involving Two Populations. Part IV: MORE INFERENTIAL STATISTICS. 11. Applications of ChiSquare. 12. Analysis of Variance. 13. Linear Correlation and Regression. 14. Elements of Nonparametric Statistics. © 2012, 368pp, Paperback, 9780538738415 www.cengageasia.com 81 STATISTICAL METHODS FOR ENGINEERS, 3E G. Geoffrey Vining, Virginia Polytechnic Institute and State University; Scott Kowalski, Minitab, Inc. STATISTICAL METHODS FOR ENGINEERS offers a balanced, streamlined one-semester introduction to Engineering Statistics that emphasizes the statistical tools most needed by practicing engineers. Using real engineering problems with real data based on actual journals and consulting experience in the field, students see how statistics fits within the methods of engineering problem solving. The text teaches students how to think like an engineer at analyzing real data and planning a project the same way they will in their careers. Case studies simulate problems students will encounter professionally and tackle on long-term job projects. The presentation makes extensive use of graphical analysis, and use of statistical software is encouraged for problem-solving to illustrate how engineers rely on computers for data analysis. The authors relate their own extensive professional experience as engineers in short margin notes called Voice of Experience that lend valuable context to how students will apply concepts in the field and why they’re important to learn. And a rich companion website provides hours of multimedia lecture presentation narrated by the authors to show the material related live by different voices, simulating how students will listen and learn from multiple colleagues in their jobs. A flexible organization allows instructors to emphasize the topics they need and cater the presentation to different engineering majors in their courses. NEW TO THIS EDITION • Innovative new multimedia lecture presentations on the companion website present a full semester’s course with audio lectures delivered by the authors and backed by slides that summarize key concepts. These presentations provide a different source for in-class lectures and self-study, and give valuable additional help to students who need more guidance. • The chapter on Control Charts and Statistical Process Control contains expanded coverage of process capability and a new formal section on process capability indices in industry. New coverage of measurement systems analysis, introduction to the concept of reliability for life time data, and BoxBehnken design have been added. • Exercises throughout the text are refreshed to include the latest data, with new exercises added where appropriate to further emphasize real data. • Additional Voice of Experience margin notes are included to enhance the utility of this popular feature. FEATURES • Most examples and exercises in the text use real engineering data taken from actual engineering journals and consulting experience, emphasizing good data analysis in specific engineering settings. Whenever possible, the text shows the full engineering context of these problems. • Voice of Experience margin notes provide snippets from the authors’ own experience as engineers about the proper application of statistics within engineering, lending students important career-related context to the material. • The text relies on the computer to do calculations, emphasizing the computer-based data analysis students will do in their careers. The book is independent of any specific software package and can be used with Microsoft® Excel, MINITAB®, JMP®, STATA®, and others. • Well over 500 exercises at the end of each section provide extensive practice that test students’ general understanding of concepts and procedures, with focus on analyzing real engineering data. Separate sets of computer exercises require statistical software for computer practice on applying concepts, performing calculations, interpreting results, and doing deeper analysis of real data. • An instructive engineering case study concludes each chapter, designed to illustrate the complex statistical decisions that need to be made to maintain the quality, consistency, and effectiveness of the process. The chapters also present ideas for small student www.cengageasia.com 82 projects complementing homework assignments, designed to simulate the way real engineers are asked to plan, execute, and interpret experiments. CONTENTS 1. ENGINEERING METHOD AND DATA COLLECTION. Need for Statistical Methods in Engineering. Engineering Method and Statistical Thinking. Statistical Thinking and Structured Problem Solving. Models. Obtaining Data. Sampling. Basic Principles of Experimental Design. Examples of Engineering Experiments. Purpose of Engineering Statistics. Case Study: Manufacture of Writing Instruments. Ideas for Projects References. 2. DATA DISPLAYS. Importance of Data Displays. Stemand-Leaf Displays. Boxplots. Using Computer Software. Using Boxplots to Analyze Designed Experiments. Case Study. Need for Probability and Distributions. Ideas for Projects References. 3. MODELING RANDOM BEHAVIOR. Probability. Random Variables and Distributions. Discrete Random Variables. Continuous Random Variables. The Normal Distribution. Random Behavior of Means. Random Behavior of Means When the Variance Is Unknown. Normal Approximation to the Binomial. The Weibull Distribution for Reliability Applications. Case Study References. 4. ESTIMATION AND TESTING. Estimation. Hypothesis Testing. Inference for a Single Mean. Inference for a Single Proportion. Inference for Two Independent Samples. The Paired t-Test. Inference for Two Proportions. Inference for Variances. Transformations and Nonparametric Analyses. Case Study. Ideas for Projects References. 5. CONTROL CHARTS AND STATISTICAL PROCESS CONTROL. Overview. Specification Limits. X- and R-Charts. X- and s²-Charts. X-Chart. np-Chart. c-Chart. Average Run Lengths. Standard Control Charts with Runs Rules. CUSUM and EWMA Charts. Basic Process Capability Indices. The SPC Approach to Gage R Studies. Case Study. Ideas for Projects References. 6. LINEAR REGRESSION ANALYSIS. Relationships Among Data. Simple Linear Regression. Multiple Linear Regression. Residual Analysis. Collinearity Diagnostics. Case Study. Ideas for Projects References. 7. INTRODUCTION TO 2k FACTORIAL-BASED EXPERIMENTS. The 2² Factorial Design. The 2k Factorial Design. Fractions of the 2k Factorial Design. Case Study. Ideas for Projects References. 8. INTRODUCTION TO RESPONSE SURFACE METHODOLOGY. Sequential Philosophy of Experimentation. Central Composite Designs. Box Behnken Designs. Multiple Responses. Experimental Designs for Quality Improvement. Case Study. Ideas for Projects References. 9. CODA. The Themes of This Course. Integrating the Themes. Statistics and Engineering. Appendix. Tables. © 2011, 648pp, Hardback, 9780538735186 STATISTICAL METHODS FOR ENGINEERS, INTERNATIONAL EDITION, 3E G. Geoffrey Vining, Virginia Polytechnic Institute and State University; Scott Kowalski, Minitab, Inc. STATISTICAL METHODS FOR ENGINEERS, 3e, International Edition offers a balanced, streamlined one-semester introduction to Engineering Statistics that emphasizes the statistical tools most needed by practicing engineers. Using real engineering problems with real data based on actual journals and consulting experience in the field, students see how statistics fits within the methods of engineering problem solving. The text teaches students how to think like an engineer at analyzing real data and planning a project the same way they will in their careers. Case studies simulate problems students will encounter professionally and tackle on long-term job projects. The presentation makes extensive use of graphical analysis, and use of statistical software is encouraged for problem-solving to illustrate how engineers rely on computers for data analysis. The authors relate their own extensive professional experience as engineers in short margin notes called Voice of Experience that lend valuable context to how students will apply concepts in the field and why they’re important to learn. And a rich companion website provides hours of multimedia lecture presentation narrated by the authors to show the material related live by different voices, simulating how students will listen and learn from multiple www.cengageasia.com 83 colleagues in their jobs. A flexible organization allows instructors to emphasize the topics they need and cater the presentation to different engineering majors in their courses. NEW TO THIS EDITION • Innovative new multimedia lecture presentations on the companion website present a full semester’s course with audio lectures delivered by the authors and backed by slides that summarize key concepts. These presentations provide a different source for in-class lectures and self-study, and give valuable additional help to students who need more guidance. • The chapter on Control Charts and Statistical Process Control contains expanded coverage of process capability and a new formal section on process capability indices in industry. New coverage of measurement systems analysis, introduction to the concept of reliability for life time data, and BoxBehnken design have been added. • Exercises throughout the text are refreshed to include the latest data, with new exercises added where appropriate to further emphasize real data. • Additional Voice of Experience margin notes are included to enhance the utility of this popular feature. FEATURES • Most examples and exercises in the text use real engineering data taken from actual engineering journals and consulting experience, emphasizing good data analysis in specific engineering settings. Whenever possible, the text shows the full engineering context of these problems. • Voice of Experience margin notes provide snippets from the authors’ own experience as engineers about the proper application of statistics within engineering, lending students important career-related context to the material. • The text relies on the computer to do calculations, emphasizing the computer-based data analysis students will do in their careers. The book is independent of any specific software package and can be used with Microsoft® Excel, MINITAB®, JMP®, STATA®, and others. • Well over 500 exercises at the end of each section provide extensive practice that test students’ general understanding of concepts and procedures, with focus on analyzing real engineering data. Separate sets of computer exercises require statistical software for computer practice on applying concepts, performing calculations, interpreting results, and doing deeper analysis of real data. • An instructive engineering case study concludes each chapter, designed to illustrate the complex statistical decisions that need to be made to maintain the quality, consistency, and effectiveness of the process. The chapters also present ideas for small student projects complementing homework assignments, designed to simulate the way real engineers are asked to plan, execute, and interpret experiments. CONTENTS 1. ENGINEERING METHOD AND DATA COLLECTION. Need for Statistical Methods in Engineering. Engineering Method and Statistical Thinking. Statistical Thinking and Structured Problem Solving. Models. Obtaining Data. Sampling. Basic Principles of Experimental Design. Examples of Engineering Experiments. Purpose of Engineering Statistics. Case Study: Manufacture of Writing Instruments. Ideas for Projects References. 2. DATA DISPLAYS. Importance of Data Displays. Stemand-Leaf Displays. Boxplots. Using Computer Software. Using Boxplots to Analyze Designed Experiments. Case Study. Need for Probability and Distributions. Ideas for Projects References. 3. MODELING RANDOM BEHAVIOR. Probability. Random Variables and Distributions. Discrete Random Variables. Continuous Random Variables. The Normal Distribution. Random Behavior of Means. Random Behavior of Means When the Variance Is Unknown. Normal Approximation to the Binomial. The Weibull Distribution for Reliability Applications. Case Study References. 4. ESTIMATION AND TESTING. Estimation. Hypothesis Testing. Inference for a Single Mean. Inference for a Single Proportion. Inference for Two Independent Samples. The Paired t-Test. Inference for Two Proportions. Inference for Variances. Transformations and Nonparametric Analyses. Case Study. Ideas for Projects References. 5. CONTROL CHARTS AND STATISTICAL PROCESS CONTROL. Overview. Specification Limits. X- and R-Charts. X- and s²-Charts. X-Chart. np-Chart. c-Chart. Average Run Lengths. Standard Control Charts with Runs Rules. CUSUM and EWMA Charts. Basic Process Capability Indices. The SPC Approach to Gage R Studies. Case Study. Ideas for Projects References. 6. LINEAR REGRESSION ANALYSIS. Relationships Among Data. www.cengageasia.com 84 Simple Linear Regression. Multiple Linear Regression. Residual Analysis. Collinearity Diagnostics. Case Study. Ideas for Projects References. 7. INTRODUCTION TO 2k FACTORIAL-BASED EXPERIMENTS. The 2² Factorial Design. The 2k Factorial Design. Fractions of the 2k Factorial Design. Case Study. Ideas for Projects References. 8. INTRODUCTION TO RESPONSE SURFACE METHODOLOGY. Sequential Philosophy of Experimentation. Central Composite Designs. BoxBehnken Designs. Multiple Responses. Experimental Designs for Quality Improvement. Case Study. Ideas for Projects References. 9. CODA. The Themes of This Course. Integrating the Themes. Statistics and Engineering. Appendix. Tables. © 2011, 648pp, Hardback, 9780538737234 STATISTICS Learning from Data (with JMP Printed Access Card) Roxy Peck, California Polytechnic State University STATISTICS: LEARNING FROM DATA, by respected and successful author Roxy Peck, resolves common problems faced by both students and instructors with an innovative approach to elementary statistics. Instead of assuming that students will “pick it up along the way,” Peck tackles the areas students struggle with most--probability, hypothesis testing, and selecting an appropriate method of analysis-unlike any text on the market. Probability coverage is based on current research that shows how students best learn the subject. Two unique chapters, one on statistical inference and another on learning from experiment data, address two common areas of student confusion: choosing a particular inference method and using inference methods with experimental data. Supported by learning objectives, real-data examples and exercises, and technology notes, this brand new text guides students in gaining conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice. FEATURES • A New Approach to Probability: Research has demonstrated how students develop an understanding of probability and chance. Using natural frequencies to reason about probability, especially conditional probability, is much easier for students to understand. The treatment of probability in this text is complete, including conditional probability and Bayes’ Rule type probability calculations. However, it’s done in a way that eliminates the need for the symbolism and formulas, which are a roadblock for many students. • Chapter on Overview of Statistical Inference (Chapter 7): This short chapter focuses on the things students need to think about in order to select an appropriate method of analysis. In most texts, these considerations are “hidden” in the discussion that occurs when a new method is introduced. Discussing these considerations up front in the form of four key questions that need to be answered before choosing an inference method makes it easier for students to make correct choices. • An Organization That Reflects the Data Analysis Process: Students are introduced early to the idea that data analysis is a process that begins with careful planning, followed by data collection, data description using graphical and numerical summaries, data analysis, and finally interpretation of results. The ordering of topics in the textbook mirrors this process: data collection, then data description, then statistical inference. • Inference for Proportions before Inference for Means: The book makes it possible to develop the concept of a sampling distribution via simulation. Simulation is simpler in the context of proportions, where it is easy to construct a hypothetical population (versus the more complicated context of means, which requires assumptions about shape and spread). In addition, inferential procedures for proportions are based on the normal distribution, allowing students to focus on the new concepts of estimation and hypothesis testing without having to grapple with the introduction of the t distribution. • Separate Treatment of Inference Based on Experiment Data (Chapter 14): Many statistical studies involve www.cengageasia.com 85 collecting data via experimentation. The same inference procedures used to estimate or test hypotheses about population parameters also are used to estimate or test hypotheses about treatment effects. However, the necessary assumptions are slightly different (for example, random assignment replaces the assumption of random selection), as is the wording of conclusions. Treating both cases together tends to confuse students; this text makes the distinction clear. • Chapter Learning Objectives--Keeping Students Informed about Expectations: The learning objectives explicitly state the expected student outcomes, and are presented in three categories: Conceptual Understanding, Mastery of Mechanics, and Putting It into Practice. • Preview--Motivation for Learning: Each chapter opens with a Preview and Preview Example that provide motivation for studying the concepts and methods introduced in the chapter. They address why the material is worth learning, provide the conceptual foundation for the methods covered in the chapter, and connect to what the student already knows. These relevant and current examples provide a context in which one or more questions are proposed for further investigation. The context is revisited in the chapter once students have the necessary understanding to more fully address the questions posed. • Real Data That Motivates and Engages: Examples and exercises with overly simple settings don’t allow students to practice interpreting results in real situations. The exercises and examples are a particular strength of this text. Most involve data extracted from journal articles, newspapers, and other published sources. They cover a wide range of disciplines and subject areas of interest to today’s students, including, among others, health and fitness, consumer research, psychology and aging, environmental research, law and criminal justice, and entertainment. • Exercises Organized into Developmental Sets to Structure the Out-of-Class Experience: End-of-section exercises are presented in two developmental sets. The exercises in each set work together to assess all of the learning objectives for the section. Additional section exercises are included for those who want more practice. • Are You Ready to Move On?--Students Test Their Understanding: Prior to moving to the next chapter, “Are You Ready to Move On?” questions allow students to confirm that they have achieved the chapter learning objectives. Like the problem sets for each section, this collection of exercises is developmental--assessing all of the chapter learning objectives and serving as a comprehensive end-ofchapter review. CONTENTS Learning from Data. Statistics--It’s All About Variability. The Data Analysis Process. Goals for Student Learning. The Structure of the Chapters that Follow. Section I: COLLECTING DATA. 1. Collecting Data in Reasonable Ways. Statistical Studies: Observation and Experimentation. Collecting Data: Planning an Observational Study. Collecting Data: Planning an Experiment. The Importance of Random Selection and Random Assignment: What Types of Conclusions are Reasonable? Section II: DESCRIBING DATA DISTRIBUTIONS. 2. Graphical Methods for Describing Data Distributions. Selecting an Appropriate Graphical Display. Displaying Categorical Data: Bar Charts and Comparative Bar Charts. Displaying Numerical Data: Dotplots, Stem-and-Leaf Displays, and Histograms. Displaying Bivariate Numerical Data: Scatterplots and Time-Series Plots. Graphical Displays in the Media. 3. Numerical Methods for Describing Data Distributions. Selecting Appropriate Numerical Summaries. Describing Center and Spread for Data Distributions that are Approximately Symmetric. Describing Center and Spread for Data Distributions that are Skewed or Have Outliers. Summarizing a Data Set: Boxplots. Measures of Relative Standing: z-scores and Percentiles. 4. Describing Bivariate Numerical Data. Correlation. Linear Regression: Fitting a Line to Bivariate Data. Assessing the Fit of a Line. Describing Linear Relationships and Making Predictions--Putting it all Together. Bonus Material on Logistic Regression (Online). Section III: A FOUNDATION FOR INFERENCE: REASONING ABOUT PROBABILITY. 5. Probability. Interpreting Probabilities. Computing Probabilities. Probabilities of More Complex Events: Unions, Intersections and Complements. Conditional Probability. Probability as a Basis for Making Decisions. Estimating Probabilities Empirically and Using Simulation (Optional). 6. Random Variables and Probability Distributions. Random Variables. Probability Distributions for Discrete Random Variables. Probability www.cengageasia.com 86 Distributions for Continuous Random Variables. The Mean and Standard Deviation of a Random Variable. The Normal Distribution. Checking for Normality. The Binomial and Geometric Distributions (Optional). Using the Normal Distribution to Approximate a Discrete Distribution (Optional). Counting Rules, The Poisson Distribution (Online). Section IV: LEARNING FROM SAMPLE DATA. 7. An Overview of Statistical Inference-Learning from Data. Statistical Inference--What We Can Learn From Data. Selecting an Appropriate Method-Four Key Questions. A Five-Step Process for Statistical Inference. 8. Sampling Variability and Sampling Distributions. Statistics and Sampling Variability. The Sampling Distribution of a Sample Proportion. How Sampling Distributions Support Learning From Data. 9. Estimating a Population Proportion. Selecting an Estimator. Estimating a Population Proportion-Margin of Error. A Large-Sample Confidence Interval for a Population Proportion. Choosing a Sample Size to Achieve a Desired Margin of Error. 10. Asking and Answering Questions about a Population Proportion. Hypotheses and Possible Conclusions. Potential Errors in Hypothesis Testing. The Logic of Hypothesis Testing-An Informal Example. A Procedure for Carrying Out a Hypothesis Test. Large-Sample Hypothesis Tests for a Population Proportion. 11. Asking and Answering Questions about the Difference between Two Population Proportions. Estimating the Difference between Two Population Proportions. Testing Hypotheses about the Difference between Two Population Proportions. 12. Asking and Answering Questions about a Population Mean. Sampling Distribution of the Sample Mean. A Confidence Interval for a Population Mean. Testing Hypotheses about a Population Mean. 13. Asking and Answering Questions about the Difference between Two Population Means. Testing Hypotheses about the Difference between Two Population Means Using Independent Samples. Testing Hypotheses about the Difference between Two Population Means Using Paired Samples. Estimating the Difference between Two Population Means. Section V: ADDITIONAL OPPORTUNITIES TO LEARN FROM DATA. 14. Learning from Experiment Data. Variability and Random Assignment. Testing Hypotheses about Differences in Treatment Effects. Estimating a Difference in Treatment Effects. 15. Learning from Categorical Data. Chi-Square Tests for Univariate Categorical Data. Tests for Homogeneity and Independence in a Two-Way Table. 16. Understanding Relationships--Numerical Data Part 2 (Online). The Simple Linear Regression Model. Inferences Concerning the Slope of the Population Regression Line. Checking Model Adequacy. 17. Asking and Answering Questions about More Than Two Means (Online). The Analysis of Variance--SingleFactor ANOVA and the F Test. Multiple Comparisons. Appendix: ANOVA Computations. © 2015, 878pp, Hardback, 9780495553267 STATISTICS, 7E The Exploration & Analysis of Data Roxy Peck, California Polytechnic State University; Jay L. Devore, California Polytechnic State University, San Luis Obispo Roxy Peck and Jay Devore’s STATISTICS: THE EXPLORATION AND ANALYSIS OF DATA, 7th Edition uses real data and attention-grabbing examples to introduce students to the study of statistics and data analysis. Traditional in structure yet modern in approach, this text guides students through an intuition-based learning process that stresses interpretation and communication of statistical information. Simple notation--including the frequent substitution of words for symbols--helps students grasp concepts and cement their comprehension. Hands-on activities and interactive applets allow students to practice statistics firsthand. NEW TO THIS EDITION • New Cumulative Review Exercises appear after selected chapter sets, allowing you to test students’ comprehension of topics spanning multiple chapters. • More than 50 new examples and more than 270 new exercises, which use data from current newspapers and journals, help students understand statistical www.cengageasia.com 87 concepts in a realistic context. In addition, more of the exercises specifically ask students to write (for example, by requiring students to explain their reasoning, interpret results, and comment on important features of an analysis). • Examples and exercises using data sets that can be accessed online from the text website (Statistics CourseMate) are designated by an icon, as are examples that are further illustrated in technology manuals for MINITAB®, SPSS®, etc.--which are also available at the website. • Exercises have been added to the “Interpreting and Communicating the Results of Statistical Analyses” sections, giving students the chance to practice these important skills. All of these sections now have assignable end-of-section questions associated with them. • More than 90 exercises have video solutions, presented by Brian Kotz of Montgomery College, which can be viewed online or downloaded for viewing later. An icon designates these exercises in the text. • A greater number of end-of-chapter activities have been added to the book, ideal for use as a chapter capstone or integrated at appropriate places as the chapter material is covered in class. • The text is enhanced by a variety of online teaching and learning resources. These include the book’s website, Statistics CourseMate; Aplia™, an online interactive learning solution that improves comprehension of key statistical concepts; and Cengage Learning’s Enhanced WebAssign®, which allows you to assign problems from the text online and ensure that students receive multimedia tutorial support as they complete their assignments. FEATURES • An optional section on logistic regression is included in Chapter 5, “Summarizing Bivariate Data,” for those who would like more complete coverage of data analysis techniques for categorical data. • Advanced topics that are often omitted in a onequarter or one-semester course, such as inference and variable selection methods in multiple regression and analysis of variance for randomized block and two-factor designs, are available online at the book’s website, Statistics CourseMate. • “Interpreting and Communicating the Results of Statistical Analysis” sections, which emphasize the importance of being able to interpret statistical output and communicate its meaning to nonstatisticians, now have assignable end-of-section questions associated with them. CONTENTS 1. THE ROLE OF STATISTICS AND THE DATA ANALYSIS PROCESS. Why Study Statistics. The Nature and Role of Variability. Statistics and the Data Analysis Process. Types of Data and Some Simple Graphical Displays. 2. COLLECTING DATA SENSIBLY. Statistical Studies: Observation and Experimentation. Sampling. Simple Comparative Experiments. More on Experimental Design. More on Observational Studies: Designing Surveys (Optional). Interpreting and Communicating the Results of Statistical Analyses. 3. GRAPHICAL METHODS FOR DESCRIBING DATA. Displaying Categorical Data: Comparative Bar Charts and Pie Charts. Displaying Numerical Data: Stem-and-Leaf Displays. Displaying Numerical Data: Frequency Distributions and Histograms. Displaying Bivariate Numerical Data. Interpreting and Communicating the Results of Statistical Analyses. 4. NUMERICAL METHODS FOR DESCRIBING DATA. Describing the Center of a Data Set. Describing Variability in a Data Set. Summarizing a Data Set: Boxplots. Interpreting Center and Variability: Chebyshev’s Rule, the Empirical Rule, and z Scores. Interpreting and Communicating the Results of Statistical Analyses. 5. SUMMARIZING BIVARIATE DATA. Correlation. Linear Regression: Fitting a Line to Bivariate Data. Assessing the Fit of a Line. Nonlinear Relationships and Transformations. Logistic Regression (Optional). Interpreting and Communicating the Results of Statistical Analyses. 6. PROBABILITY. Interpreting Probabilities and Basic Probability Rules. Probability as a Basis for Making Decisions. Estimating Probabilities Empirically and by Using Simulation. 7. RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS. Describing the Distribution of Values in a Population. Population Models for Continuous Numerical Variables. Normal Distributions. Checking for Normality and Normalizing Transformations. 8. SAMPLING VARIABILITY AND SAMPLING DISTRIBUTION. Statistics and Sampling Variability. The Sampling Distribution of a Sample Mean. The Sampling Distribution of a Sample Proportion. 9. ESTIMATION USING A SINGLE SAMPLE. Point Estimation. Large-Sample Confidence Interval for www.cengageasia.com 88 a Population Proportion. Confidence Interval for a Population Mean. Interpreting and Communicating the Results of Statistical Analyses. 10. HYPOTHESIS TESTING USING A SINGLE SAMPLE. Hypotheses and Test Procedures. Errors in Hypotheses Testing. Large-Sample Hypothesis Tests for a Population Proportion. Hypotheses Tests for a Population Mean. Power and Probability of Type II Error. Interpreting and Communicating the Results of Statistical Analyses. 11. COMPARING TWO POPULATIONS OR TREATMENTS. Inferences Concerning the Difference Between Two Population or Treatment Means Using Independent Samples. Inferences Concerning the Difference Between Two Population or Treatment Means Using Paired Samples. Large Sample Inferences Concerning a Difference Between Two Population or Treatment Proportions. Interpreting and Communicating the Results of Statistical Analyses. 12. THE ANALYSIS OF CATEGORICAL DATA AND GOODNESS-OF-FIT TESTS. Chi-Square Tests for Univariate Data. Tests for Homogeneity and Independence in a Two-way Table. Interpreting and Communicating the Results of Statistical Analyses. 13. SIMPLE LINEAR REGRESSION AND CORRELATION: INFERENTIAL METHODS. Simple Linear Regression Model. Inferences About the Slope of the Population Regression Line. Checking Model Adequacy. Inferences Based on the Estimated Regression Line (Optional). Inferences About the Population Correlation Coefficient (Optional). Interpreting and Communicating the Results of Statistical Analyses. 14. MULTIPLE REGRESSION ANALYSIS. Multiple Regression Models. Fitting a Model and Assessing Its Utility. Inferences Based on an Estimated Model (online). Other Issues in Multiple Regression (online). Interpreting and Communicating the Results of Statistical Analyses (online). 15. ANALYSIS OF VARIANCE. Single-Factor ANOVA and the F Test. Multiple Comparisons. The F Test for a Randomized Block Experiment (online). Two-Factor ANOVA (online). Interpreting and Communicating the Results of Statistical Analyses (online). 16. NONPARAMETRIC (DISTRIBUTION-FREE STATISTICAL METHODS (ONLINE). Distribution-Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Independent Samples (Optional). Distribution-Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Paired Samples. Distribution- Free ANOVA. © 2012, 816pp, Hardback, 9780840058010 STATISTICS, INTERNATIONAL EDITION, 4E Robert F. Heckard, Pennsylvania State University; Jessica M. Utts, University of California, Irvine STATISTICS, 4e, International Edition emphasizes the conceptual development of statistical ideas and the importance of looking for—and finding—meaning in data. Authors Jessica Utts and Robert Heckard actively engage students’ natural curiosity, motivating them with intriguing questions and explaining statistical topics in the context of a wide range of interesting, useful examples and case studies. Throughout, the authors balance the promotion of statistical literacy with coverage of the statistical methodology taught in general introductory statistics courses. Their approach is based on the premises that new material is much easier to learn and remember if it is related to something interesting or previously known, and if students actively ask questions and find the answers for themselves. More than any other text available, STATISTICS, 4e, International Edition motivates and encourages students to develop their statistical intuition by focusing on analyzing data and interpreting results, rather than on mathematical formulation. CourseSmart goes beyond traditional expectations– providing instant, online access to the textbooks and course materials you need and at a lower cost to your students. To request an electronic sample of this Cengage Learning title, go to: www.coursesmart.com/ instructors. NEW TO THIS EDITION • To create a more natural flow of topic coverage, www.cengageasia.com 89 Chapters 5 and 6 have been moved to precede Chapters 3 and 4 so that the material on describing data comes before the two chapters on collecting data. • The language has been tightened and simplified in all chapters whenever possible. In particular, Chapters 9, 10, and 12 have been substantially honed. • New topics in this edition include multiple comparisons in Chapters 1 and 13, power curves in Chapters 12 and 13, and McNemar’s test in Chapter 15. • The exercise sets have been significantly reorganized and rewritten. For example, in response to reviewers’ suggestions, many exercises are now set up in oddeven pairs, featuring odd-numbered problems--which are answered in the back of the book--followed by similar even-numbered problems. In addition, a number of new problems have been added, including a significant number of new drill exercises. • The data in the problem sets, examples, and case studies have been updated with the latest possible information. In addition, the book includes all-new examples and case studies as well as new and updated data sets. • A greater number of In Summary boxes are placed consistently throughout each chapter, reinforcing key ideas as students read. A listing of the In Summary boxes now appears at the end of the chapter, making these features even more helpful as study aids. • Aplia™ is available with the new edition. This online interactive learning solution--which improves student comprehension and outcomes with detailed, immediate explanations--also saves you time by automatically grading assignments. FEATURES • Chapters 9–13, containing the core material on sampling distributions and statistical inference, are organized in a modular, flexible format. There are six modules for each of the topics of sampling distributions, confidence intervals, and hypothesis testing. The first module provides an introduction and each of the remaining five modules deals with a specific parameter (one mean, one proportion, etc.). This modular format emphasizes the similarity among the inference procedures for the five parameters discussed, and allows instructors to cover this material in any order they choose. • Technology Tips appear throughout the text for MINITAB®, SPSS®, Excel®, JMP, and TI-83/84 calculators. Technical manuals for these technologies, as well as for JMP and R, are available on the book’s website, Statistics CourseMate. • Original journal articles for selected case studies and examples (identified in the text by an icon) can be found on the book’s website, Statistics CourseMate. Reading these original articles enables students to learn more about how the research was conducted, what methods were used, and the conclusions drawn. CONTENTS 1. STATISTICS SUCCESS STORIES AND CAUTIONARY TALES. What Is Statistics? Eight Statistical Stories with Morals. The Common Elements in the Eight Stories. 2. TURNING DATA INTO INFORMATION. Raw Data. Types of Variables. Summarizing One or Two Categorical Variables. Exploring Features of Quantitative Data with Pictures. Numerical Summaries of Quantitative Variables. How to Handle Outliers. Bell-Shaped Distributions and Standard Deviations. Skillbuilder Applet: The Empirical Rule in Action. 3. RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES. Looking for Patterns with Scatterplots. Describing Linear Patterns with a Regression Line. Measuring Strength and Direction with Correlation. Regression and Correlation Difficulties and Disasters. Correlation Does Not Prove Causation. Skillbuilder Applet: Exploring Correlation. 4. RELATIONSHIPS BETWEEN CATEGORICAL VARIABLES. Displaying Relationships Between Categorical Variables. Risk, Relative Risk, and Misleading Statistics About Risk. The Effect of a Third Variable and Simpson’s Paradox. Assessing the Statistical Significance of a 2 × 2 Table. 5. SAMPLING: SURVEYS AND HOW TO ASK QUESTIONS. Collecting and Using Sample Data Wisely. Margin of Error, Confidence Intervals, and Sample Size. Choosing a Simple Random Sample. Other Sampling Methods. Difficulties and Disasters in Sampling. How to Ask Survey Questions. Skillbuilder Applet: Random Sampling in Action. 6. GATHERING USEFUL DATA FOR EXAMINING RELATIONSHIPS. Speaking the Language of Research Studies. Designing a Good Experiment. Designing a Good Observational Study. Difficulties and Disasters in Experiments and Observational Studies. 7. PROBABILITY. Random Circumstances. Interpretations of Probability. Probability Definitions and Relationships. Basic Rules for Finding Probabilities. Finding Complicated Probabilities. Using Simulation www.cengageasia.com 90 to Estimate Probabilities. Flawed Intuitive Judgments About Probability. 8. RANDOM VARIABLES. What Is a Random Variable? Discrete Random Variables. Expectations for Random Variables. Binomial Random Variables. Continuous Random Variables. Normal Random Variables. Approximating Binomial Distribution Probabilities. Sums, Differences, and Combinations of Random Variables. 9. UNDERSTANDING SAMPLING DISTRIBUTIONS: STATISTICS AS RANDOM VARIABLES. Parameters, Statistics, and Statistical Inference. From Curiosity to Questions About Parameters. SD Module 0: An Overview of Sampling Distributions. SD Module 1: Sampling Distribution for One Sample Proportion. SD Module 2: Sampling Distribution for the Difference in Two Sample Proportions. SD Module 3: Sampling Distribution for One Sample Mean. SD Module 4: Sampling Distribution for the Sample Mean of Paired Differences. SD Module 5: Sampling Distribution for the Difference in Two Sample Means. Preparing for Statistical Inference: Standardized Statistics. Generalizations Beyond the Big Five. Skillbuilder Applet: Finding the Pattern in Sample Means. 10. ESTIMATING PROPORTIONS WITH CONFIDENCE. CI Module 0: An Overview of Confidence Intervals. CI Module 1: Confidence Interval for a Population Proportion. CI Module 2: Confidence Intervals for the Difference in Two Population Proportions. Using Confidence Intervals to Guide Decisions. 11. ESTIMATING MEANS WITH CONFIDENCE. Introduction to Confidence Intervals for Means. CI Module 3: Confidence Interval for One Population Mean. CI Module 4: Confidence Interval for the Population Mean of Paired Differences. CI Module 5: Confidence Interval for the Difference in Two Population Means (Independent Samples). Understanding Any Confidence Interval. Skillbuilder Applet: The Confidence Level in Action. 12. TESTING HYPOTHESES ABOUT PROPORTIONS. HT Module 0: An Overview of Hypothesis Testing. HT Module 1: Testing Hypotheses About a Population Proportion. HT Module 2: Testing Hypotheses About the Difference in Two Population Proportions. Sample Size, Statistical Significance, and Practical Importance. 13. TESTING HYPOTHESES ABOUT MEANS. Introduction to Hypothesis Tests for Means. HT Module 3: Testing Hypotheses about One Population Mean. HT Module 4: Testing Hypotheses about the Population Mean of Paired Differences. HT Module 5: Testing Hypotheses about the Difference in Two Population Means (Independent Samples). The Relationship Between Significance Tests and Confidence Intervals. Choosing an Appropriate Inference Procedure. Effect Size. Evaluating Significance in Research Reports. 14. INFERENCE ABOUT SIMPLE REGRESSION. Sample and Population Regression Models. Estimating the Standard Deviation for Regression. Inference About the Slope of a Linear Regression. Predicting y and Estimating Mean y at a Specific x. Checking Conditions for Using Regression Models for Inference. 15. MORE ABOUT INFERENCE FOR CATEGORICAL VARIABLES. The Chi-Square Test for Two-Way Tables. Analyzing 2 × 2 Tables. Testing Hypotheses About One Categorical Variable: Goodness-of-Fit. 16. ANALYSIS OF VARIANCE. Comparing Means with an ANOVA F-Test. Details of One-Way Analysis of Variance. Other Methods for Comparing Populations. Two-Way Analysis of Variance. 17. TURNING INFORMATION INTO WISDOM. Beyond the Data. Transforming Uncertainty Into Wisdom. Making Personal Decisions. Control of Societal Risks. Understanding Our World. Getting to Know You. Words to the Wise. © 2012, 800pp, Paperback, 9780538735919 STATISTICS, INTERNATIONAL EDITION, 10E Charles Henry Brase, Regis University; Corrinne Pellillo Brase, Arapahoe Community College STATISTICS, 10e, International Edition is a thorough, yet accessible program designed to help students overcome their apprehensions about statistics. The authors provide clear guidance and informal advice while showing students the links between statistics and the world. To reinforce this approach, the book integrates real-life data from a variety of sources, including journals, periodicals, newspapers, and the www.cengageasia.com 91 Internet. The Tenth Edition continues to address the importance of developing students’ critical thinking and statistical literacy skills through special features and exercises throughout the text. The use of graphing calculators, Excel®, MINITAB®, and SPSS® is covered though not required. Extensive technology resources include an algorithmic test bank and lecture slides, along with interactive online resources and a marketleading DVD series designed to provide reinforcement for students and support for instructors. NEW TO THIS EDITION • Updated technology-related examples include instruction appropriate for the latest versions of MINITAB®, SPSS®, and Excel®. Denoted by icons in the text, “Tech Notes!” provide best practices and accompanying screenshots to assist students who are using these programs. • Updated real-world applications throughout the text include new examples from a variety of disciplines. • A new “Foreshadowing” feature alerts students to additional related material to be presented in subsequent “Expand Your Knowledge” problems. Uniform probability distribution and the exponential probability distribution are introduced in the “Expand Your Knowledge” section of Chapter 6, “Normal Distributions.” Polynomial regression (curvilinear regression) is discussed in the “Expand Your Knowledge” section of Chapter 9, “Correlation and Regression.” • A new “Looking Ahead” feature gives students a taste of forthcoming topics--showing them how the concepts and skills they’re reading about will be useful in helping them grasp material covered later. • New problems featuring basic computation and using small data sets give students the chance to appreciate the formulas and mathematical processes that their calculators accomplish for them--and help them to better understand what the end result means. • An updated Chapter 6, “Normal Distributions,” now covers this topic as well as sampling distributions to streamline the course and provide an immediate, important application of the normal distribution. The chapter also covers the normal approximation to the binomial distribution. • Instructions for using the TI-Nspire calculator are included in the TI technology guide (available online). • Updated and revised examples in Chapter 4, “Probability,” incorporate sample spaces that are small and easy for students to visualize. • Examples, guided exercises, and problem sets throughout the text feature interpretation. They also address the suitability of using a specified statistical method or process for analysis. FEATURES • The text is enhanced by a variety of online teaching and learning resources. These include the book’s website, Statistics CourseMate; Aplia™, an online interactive learning solution that improves comprehension of key statistical concepts; and Cengage Learning’s Enhanced WebAssign®, which allows you to assign problems from the text online and ensure that students receive multimedia tutorial support as they complete their assignments. CONTENTS Note: Each chapter concludes with a Summary, Important Words and Symbols, Chapter Review Problems, Data Highlights: Group Projects, Linking Concepts: Writing Projects, and Using Technology. Table of Prerequisite Material. 1. GETTING STARTED. Focus Problem: Where Have All the Fireflies Gone? What Is Statistics. Random Samples. Introduction to Experimental Design. 2. ORGANIZING DATA. Focus Problem: Say It with Pictures. Frequency Distributions, Histograms, and Related Topics. Bar Graphs, Circle Graphs, and Time-Series Graphs. Stem-and-Leaf Displays. 3. AVERAGES AND VARIATION. Focus Problem: The Educational Advantage. Measures of Central Tendency: Mode, Mediate, and Mean. Measures of Variation. Percentiles and Box-andWhisker Plots. Cumulative Review Problems: Chapters 1–3. 4. ELEMENTARY PROBABILITY THEORY. Focus Problem: How Often Do Lie Detectors Lie? What Is Probability? Some Probability Rules—Compound Events. Trees and Counting Techniques. 5. THE BINOMIAL PROBABILITY DISTRIBUTION AND RELATED TOPICS. Focus Problem: Personality Preference Types: Introvert or Extrovert? Introduction to Random Variables and Probability Distributions. Binomial Probabilities. Additional Properties of the Binomial Distribution. The Geometric and Poisson Probability Distributions. 6. NORMAL DISTRIBUTIONS. Focus Problem: Impulse Buying. Graphs of Normal Probability Distributions. Standard Units and Areas Under the Standard Normal www.cengageasia.com 92 Distribution. Areas Under Any Normal Curve. Normal Approximation to the Binomial Distribution. Sampling Distributions. The Central Limit Theorem. Cumulative Review Problems: Chapters 4-6. 7. ESTIMATION. Focus Problem: The Trouble Is Unknown.Is Known. Estimating µ When  with Wood Ducks. Estimating µ When Estimating p in the Binomial Distribution. Estimating µ 1-µ 2 and p1-p2. 8. HYPOTHESIS TESTING. Focus Problem: Benford’s Law: The Importance of Being Number 1. Introduction to Statistical Tests. Testing the Mean µ. Testing a Proportion p. Tests Involving Paired Differences (Dependent Samples). Testing µ1-µ2 and p1-p2 (Independent Samples). 9. CORRELATION AND REGRESSION. Focus Problem: Changing Populations and Crime Rate. Scatter Diagrams and Linear Correlation. Linear Regression and the Coefficient of Determination. Inferences for Correlation and Regression. Multiple Regression. Cumulative Review Problems: Chapters 7-9. 10. CHI-SQUARE AND F DISTRIBUTIONS. Focus Problem: Archaeology in Bandelier National Monument. Part I: Inferences Using the Chi-Square Distribution. Overview of the Chi-Square Distribution. Chi-Square: Tests of Independence and of Homogeneity. ChiSquare: Goodness of Fit. Testing and Estimating a Single Variance or Standard Deviation. Part II: Inferences Using the F Distribution. Testing Two Variances. OneWay ANOVA: Comparing Several Sample Means. Introduction to Two-Way ANOVA. 11. NONPARAMETRIC STATISTICS. Focus Problem: How Cold? Compared to What? The Sign Test for Matched Pairs. The RankSum Test. Spearman Rank Correlation. Runs Test for Randomness. Cumulative Review Problems: Chapters 10-12. APPENDIX I: ADDITIONAL TOPICS. Bayes’s Theorem. The Hypergeometric Probability Distribution. APPENDIX II: TABLES. Random Numbers. Binomial Coefficients. Binomial Probability Distribution. Poisson Probability Distribution. Areas of a Standard Normal Distribution. Critical Values for Student’s t Distribution. The X2 Distribution. Critical Values for F Distribution. Critical Values for Spearman Rank Correlation. Critical Values for Number of Runs R. © 2012, 832pp, Paperback, 9781111570040 UNDERSTANDABLE STATISTICS, 11E Charles Henry Brase, Regis University; Corrinne Pellillo Brase, Arapahoe Community College UNDERSTANDABLE STATISTICS: CONCEPTS AND METHODS, Eleventh Edition, is a thorough yet accessible program designed to help students overcome their apprehensions about statistics. The authors provide clear guidance and informal advice while showing students the links between statistics and the world. To reinforce this approach, the book integrates real-life data from a variety of sources, including journals, periodicals, newspapers, and the Internet. The eleventh edition continues to address the importance of developing students’ critical-thinking and statistical literacy skills through special features and exercises throughout the text. The use of graphing calculators, Excel®, MINITAB®, and SPSS® is covered although not required. Extensive technology resources include an algorithmic Test Bank and lecture slides, along with interactive online resources and a marketleading DVD series designed to provide reinforcement for students and support for instructors. NEW TO THIS EDITION • “What Does . . . Tell Us?” and “Important Features of a . . .” provide brief just-in-time summaries of key concepts. • New examples and exercises were written for the new edition. • The language has been tightened and simplified whenever possible. FEATURES • Up-to-date technology-related examples include instruction appropriate for the latest versions of MINITAB®, SPSS®, and Excel®. Denoted by icons in the text, “Tech Notes!” provide best practices and www.cengageasia.com 93 accompanying screenshots to assist students who are using these programs. • Updated real-world applications throughout the text include new examples from a variety of disciplines. • The “Foreshadowing” feature alerts students to additional related material to be presented in subsequent “Expand Your Knowledge” problems. Uniform probability distribution and the exponential probability distribution are introduced in the “Expand Your Knowledge” section of Chapter 6, “Normal Distributions.” Polynomial regression (curvilinear regression) is discussed in the “Expand Your Knowledge” section of Chapter 9, “Correlation and Regression.” • The “Looking Ahead” feature gives students a taste of forthcoming topics--showing them how the concepts and skills they’re reading about will be useful in helping them grasp material covered later. • Problems featuring basic computation and using small data sets give students the chance to appreciate the formulas and mathematical processes that their calculators accomplish for them--and help them to better understand what the end result means. • Chapter 6, “Normal Distributions,” covers this topic as well as sampling distributions to streamline the course and provide an immediate, important application of the normal distribution. The chapter also covers the normal approximation to the binomial distribution. • Instructions for using the TI-Nspire calculator are included in the TI technology guide (available online). CONTENTS 1. Getting Started. 2. Organizing Data. 3. Averages and Variation. 4. Elementary Probability Theory. 5. The Binomial Probability Distribution and Related Topics. 6. Normal Curves and Sampling Distributions. 7. Estimation. 8. Hypothesis Testing. 9. Correlation and Regression. 10. Chi-Square and F Distributions. 11. Nonparametric Statistics. © 2015, 864pp, Hardback, 9781285460918 UNDERSTANDING BASIC STATISTICS, INTERNATIONAL EDITION, 6E Charles Henry Brase, Regis University; Corrinne Pellillo Brase, Arapahoe Community College Help students overcome their apprehension about statistics Brase and Brase’s UNDERSTANDING BASIC STATISTICS, 6E, International Edition. A condensed and more streamlined version of the same authors’ bestselling STATISTICS, 10E, International Edition this book offers instructors an effective way to teach the essentials of statistics, including early coverage of regression, within a more limited time frame. Thorough yet abbreviated, the text helps students realize the real-world significance of statistics, and offers an accessible exposition and a new, complete technology package. The UNDERSTANDING BASIC STATISTICS, 6E, International Edition addresses the growing importance of developing students’ critical thinking and statistical literacy skills with the introduction of new features and exercises throughout the text. The use of the graphing calculator, Microsoft® Excel®, MINITAB®, and SPSS is covered but not required. NEW TO THIS EDITION • NEW Complement your text and course content with study and practice materials. Cengage Learning’s Statistics CourseMate brings course concepts to life with interactive learning, study, and exam preparation tools that support the printed textbook. Watch student comprehension soar as your class works with the printed textbook and the textbook-specific website. Statistics CourseMate goes beyond the book to deliver what you need! • NEW More than 200 new and revised problems that feature basic computations, interpretation, and reinforce statistical literacy. Basic computation problems focus student attention on relevant formulas, www.cengageasia.com 94 requirements, and computational procedures. After practicing these skills, students are more confident as they approach real-world applications. • NEW The Looking Forward feature shows students where the presented material will be used later and motivates them to pay extra attention to key topics. • NEW Interpretation problems ask students to explain the meaning of statistical results in the context of a particular application. • NEW Expand Your Knowledge problems present the geometric mean and the harmonic mean (Section 3.1), moving averages (Section 3.2), correlation of averages (Section 4.2), odds for and odds against (Section 5.1), uniform probability distribution (Section 7.1), plus four method for confidence intervals (Section 8.3), probability of two confidence intervals (Chapter 8 review), and serial correlation (Section 11.4). • Section 2.1 now includes a discussion of cumulative frequency and ogives. • Problem sets in Sections 3.1 and 3.2 provide problems discussing the effects of data transformation (adding a constant to each data value or multiplying each data value by a constant) on the mean, median, mode, and standard deviation. • Examples in Probability (Chapter 5) have been revised to incorporate sample spaces that are small and easy for students to visualize. • REVISED Tech Notes throughout the text give students helpful hints on using TI-84 Plus and TI-Nspire (with 84 Plus keypad) and TI-83 calculators, Microsoft Excel 2010, and MINITAB to solve a problem. They include display screens to help students visualize and better understand the solution. • REVISED Using Technology instructions are available at the end of each chapter in the Using Technology section. Problems are presented with real-world data from a variety of disciplines that can be solved by using TI-84 Plus, TI-Nspire (with 84 Plus keypad) and TI-83 Plus calculators, Microsoft Excel 2010, MINITAB, and SPSS. of Central Tendency: Mode, Median, and Mean. Measures of Variation. Percentiles and Box-and-Whisker Plots. 4. CORRELATION AND REGRESSION. Scatter Diagrams and Linear Correlation. Linear Regression and the Coefficient of Determination. 5. ELEMENTARY PROBABILITY THEORY. What is Probability? Some Probability Rules—Compound Events. Trees and Counting Techniques. 6. THE BINOMIAL PROBABILITY DISTRIBUTION AND RELATED TOPICS. Introduction to Random Variables and Probability Distributions. Binomial Probabilities. Additional Properties of the Binomial Distribution. 7. NORMAL CURVES AND SAMPLING DISTRIBUTIONS. Graphs and Normal Probability Distributions. Standard Units and Areas Under the Standard Normal Distribution. Areas Under Any Normal Curve. Sampling Distributions. The Central Limit Theorem. Normal Approximation to the Binomial Distribution and to p^ Distribution. 8. ESTIMATION. Estimating µ When σ Is Known. Estimating µ When σ is Unknown. Estimating p in a Binomial Distribution. 9. HYPOTHESIS TESTING. Introduction to Statistical Tests. Testing the Mean µ. Testing a Proportion p. 10. INFERENCES ABOUT DIFFERENCES. Tests Involving Paired Differences (Dependent Samples). Inferences About the Difference of Two Means µ1 – µ2. Inferences About the Difference of Two Proportions p1 – p2. 11. ADDITIONAL TOPICS USING INFERENCE. ChiSquare: Tests of Independence and of Homogeneity. Chi-Square: Goodness of Fit. Testing and Estimating a Single Variance or Standard Deviation. Inferences for Correlation and Regression. © 2013, 640pp, Paperback, 9781133109983 CONTENTS 1. GETTING STARTED. What is Statistics? Random Samples. Introduction to Experimental Design. 2. ORGANIZING DATA. Frequency Distributions, Histograms, and Related Topics. Bar Graphs, Circle Graphs, and Time-Series Graphs. Stem-and-Leaf Displays. 3. AVERAGES AND VARIATION. Measures www.cengageasia.com 95 Software / Handbooks / Courseware / Casebooks LEARNING SAS IN THE COMPUTER LAB, 3E Rebecca J. Elliott; Christopher H. Morrell, Loyola University Maryland LEARNING SAS IN THE COMPUTER LAB, 3RD EDITION, gets students up and running quickly with SAS. Explaining concepts and commands in clear steps, the authors provide SAS basics so students can successfully apply computing techniques to all types of statistics problems and acquire an appreciation of data analysis. This brief, affordable manual also ensures that students learn enough about programming with SAS to be able to find their way around the many SAS reference manuals that they will use in their professional activities. NEW TO THIS EDITION • Current – The new edition features code and graphics that are up-to-date with a more recent version of SAS while maintaining the book’s clear, accessible coverage of SAS basics. • New data sets and problems—Two data sets have been added; most modules have additional problems to provide students with ample opportunities to practice programming. • New modules—A new module covers matrix computations in SAS using IML; another new module introduces macro variables and macro programs. • Microsoft® Excel instructions—Instructions are now included on how to import/export data from and to Microsoft Excel. FEATURES • Modular—Instructors can easily emphasize different points in statistics as appropriate for their courses. The 25 modules can be used in almost any order and the book works well with any introductory statistics text; students do not need to know calculus. • Realistic—Each module contains several problems that ask students to write a SAS program to analyze a set of data. Nearly all of the data sets (many collected by students for a statistics class) are real, enabling students to understand the connection of the material to the real world. Data sets are described in the Appendix; files can be found on the Book Companion Website. • Clear explanations with visuals—Each of 25 modules contains explanations of the appropriate SAS code and tips on what to look for on the output SAS generates. Examples of input and output let students see what data looks like in SAS. In addition, a tear-out road map (flowchart) at the back of the book explains which SAS procedures to use for different kinds of statistical analysis. • Flexible—The material can be used in a lab session or as a self-study aid for learning SAS. CONTENTS 1. THE BASICS. Data Set Structure. Data Steps and PROC Steps. The Data Step. PROC PRINT. The Input Statement. Comparison of Character Formats. SAS Program, Log, and List Files. Problems. 2. MORE SAS BASICS. Subsetting If. If. . . Then . . . Else Statements. PROC FORMAT. Subsetting Where. Using Data from Outside Files. Missover Option on Infile Statement. PROC IMPORT for Microsoft® Excel Files. PROC SORT. Set Statement. Problems. 3. DATA MANAGEMENT. How SAS “Thinks.” Lag Function. Drop and Keep Statements. Combining Data Sets. Comment Lines. Limiting the Number of Observations Read from External Files. Setting the Size of the Output. The Put Statement. PROC EXPORT for Microsoft Excel Files. Problems. 4. SAS FUNCTIONS. Numeric Functions. Character Functions. Probability Functions. Descriptive Statistics Functions. Problems. 5. DESCRIPTIVE STATISTICS I. Label Statements. Title Statements. PROC UNIVARIATE. PROC BOXPLOT. Problems. 6. PROC GCHART. PROC GCHART. Problems. 7. DESCRIPTIVE STATISTICS II. PROC MEANS. PROC FREQ. Problems. 8. GENERATING RANDOM OBSERVATIONS. SAS Distributional Functions. Do Loops. Problems. 9. X-Y PLOTS. PROC GPLOT. Problems. 10. ONE-SAMPLE TESTS FOR µ, P. One-Sample t-Tests. One-Sample Test for P. Problems. 11. TWO-SAMPLE T-TESTS. PROC TTEST. T-Tests: www.cengageasia.com 96 Two-Sample, Pooled, and Paired. Problems. 12. ONEWAY ANOVA. Analysis of Variance—PROC GLM and PROC ANOVA. Problems. 13. TWO-WAY ANOVA AND MORE. Two-Way ANOVA. General ANOVA Analysis. Problems. 14. MODEL CHECKING IN ANOVA. Plots for Model Checking. More on PROC GLM. Problems. 15. CORRELATIONS. PROC CORR. Problems. 16. SIMPLE LINEAR REGRESSION PROC REG. Problems. 17. MODEL CHECKING IN REGRESSION. Plots for Model Checking. More on PROC REG. Problems. 18. MULTIPLE LINEAR REGRESSION. Multiple Regression. Problems. 19. MULTIPLE REGRESSION: AIDS FOR SELECTING AND ASSESSING MODELS. Statistics Used in Multiple Regression Model Checking. PROC REG Selection = Option. Problems. 20. TESTS FOR CATEGORICAL DATA. Chi-Square Tests. Problems. 21. NONPARAMETRIC TESTS. One-Sample Tests. Tests for Two or More Samples. Problems. 22. ANALYSIS OF COVARIANCE. Using PROC GLM for ANCOVA. Problems. 23. LOGISTIC REGRESSION PROC LOGISTIC. Problems. 24. MATRIX COMPUTATIONS. PROC IML. Problems. 25. MACRO VARIABLES AND PROGRAMS. Macro Variables. Macro Programs. Problems. DESCRIPTION OF DATA SETS. INDEX. © 2010, 320pp, Paperback, 9780495559689 STATISTICS WITH STATA, 8E Version 12, International Edition Lawrence C. Hamilton, University of New Hampshire For students and practicing researchers alike, STATISTICS WITH STATA Version 12, International Edition opens the door to the full use of the popular Stata program—a fast, flexible, and easy-to-use environment for data management and statistics analysis. Integrating Stata’s impressive graphics, this comprehensive book presents hundreds of examples showing how to apply Stata to accomplish a wide variety of tasks. Like Stata itself, STATISTICS WITH STATA Version 12, International Edition will make it easier for readers to move fluidly through the world of modern data analysis. NEW TO THIS EDITION • The text has been updated throughout to reflect recent enhancements to Stata commands, output, and features—providing readers with a reliable, up-to-date guide to the current software package that is ideal for use in the classroom, lab, or office. FEATURES • Chapters on Survey Data Analysis and Multilevel and Mixed-Effects Modeling provide detailed guidelines to help users appreciate and apply the sophisticated capabilities that the latest version of Stata offers in these areas. • Sections in many chapters provide additional information or emphasis reflecting the needs of today’s real-world users and the growing capabilities of Stata, including material on extended missing value codes, the Graph Editor and Creative Graphing, and a demonstration of ARMAX time series regression models. • The final chapter contains two sections to help advanced Stata users create even more precise and powerful programs: an example program that draws multiple graphs for reporting survey results, and a first look at matrix programming with Mata. • Methods for drawing time plots, fitting nonlinear models, estimating standard errors, and obtaining predictions or diagnostic statistics complement additional options provided for long-established commands, giving users even more options to make the most of their work with Stata. • With full integration of Stata’s graphics, this comprehensive book provides a thorough conceptual and practical overview of the software in an especially easy-to-follow, visually appealing way, making it easier to plan and perform analysis with Stata. • Bridging the gap between statistics textbooks and software reference manuals, this immensely practical and reader-friendly text offers a solid introduction to important statistical concepts and how to work with them in Stata, providing an indispensable learning and reference tool for students and professional www.cengageasia.com 97 researchers alike. • Readers of this one-of-a-kind text benefit from the unique insights and expertise Lawrence Hamilton provides, based on his more than 25 years of using, writing about, and teaching statistics courses at all levels with Stata. • The first six chapters roughly mirror an introductory statistics course, while the second half of the text explores more complex material (including the basics of programming with Stata) appropriate for advanced courses or original research, making it easy to use the text effectively in a variety of classes and contexts. • For readers beginning to explore more advanced material, basic information and illustrations provide an introduction to topics such as how to obtain and interpret diagnostic statistics and graphs and how to perform robust, quantile, nonlinear, logit, ordered logit, multinomial logit, or Poisson regression. CONTENTS Preface. 1. Stata and Stata Resources. 2. Data Management. 3. Graphs. 4. Summary Statistics and Tables. 5. ANOVA and other Comparison Methods. 6. Linear Regression Analysis. 7. Regression Diagnostics. 8. Fitting Curves. 9. Robust Regression. 10. Logistic Regression. 11. Survival and Event-Count Models. 12. Principal Components, Factor, and Cluster Analysis. 13. Time Series Analysis. 14. Survey Data Analysis. 15. Multilevel and Mixed-Effects Modeling. 16. Introduction to Programming. References. Index. © 2013, 496pp, Paperback, 9781111580728 www.cengageasia.com 98 Combined Author/Title Index A An Introduction to Statistical Methods and Data Analysis, 6e, p.22 An Introduction to Statistical Methods and Data Analysis, International Edition, 6e, p.23 Applied Regression Analysis and Multivariable Methods, International Edition, 4e, p.26 Applied Regression Analysis and Other Multivariable Methods, 4e, p.28 Applied Regression Analysis and Other Multivariable Methods, 5e, p.29 Applied Statistics for Engineers and Scientists, 3e, p.46 B Brase/Brase, Statistics, International Edition, 10e, p.18/p.91 Brase/Brase, Understandable Statistics, 11e, p.93 Brase/Brase, Understanding Basic Statistics, International Edition, 6e, p.94 D Devore, Probability and Statistics for Engineering and the Sciences, 8e, p.67 Devore, Probability and Statistics for Engineering and the Sciences, International Edition, 8e, p.68 Devore/Farnum/Doi, Applied Statistics for Engineers and Scientists, 3e, p.46 J Johnson/Kuby, Elementary Statistics, 11e, p.6/p.52 Johnson/Kuby, Elementary Statistics, Enhanced Review Edition (w/CD-ROM & CengageNOW, InfoTrac® 2-Sem., iLrn® Homework, Personal Tutor, Internet Companion for Statistics 2-Sem. PAC), 10e, p.7/p.53 Johnson/Kuby, Elementary Statistics, International Edition, 11e, p.9/p.55 Johnson/Kuby, STAT 2 (with Review Cards and CourseMate Printed Access Card), 2e, p.15/p.81 K Kleinbaum/Kupper/Nizam/Muller, Applied Regression Analysis and Multivariable Methods, International Edition, 4e, p.26 Kleinbaum/Kupper/Nizam/Muller, Applied Regression Analysis and Other Multivariable Methods, 4e, p.28 Kleinbaum/Kupper/Nizam/Rosenberg, Applied Regression Analysis and Other Multivariable Methods, 5e, p.29 L E Elementary Statistics, p.2/p.48 Elementary Statistics, International Edition, p.4/p.50 Elementary Statistics, 11e, p.6/p.52 Elementary Statistics, Enhanced Review Edition (w/CD-ROM & CengageNOW, InfoTrac® 2-Sem., iLrn® Homework, Personal Tutor, Internet Companion for Statistics 2-Sem. PAC), 10e, p.7/p.53 Elementary Statistics, International Edition, 11e, p.9/p.55 Elementary Survey Sampling, 7e, p.32 Elliott/Morrell, Learning SAS in the Computer Lab, 3e, p.96 F Fundamentals of Biostatistics, 7e, p.56 Fundamentals of Biostatistics, International Edition, 7e, p.57 H Hamilton, Statistics with STATA, 8e, p.97 Hayter, Probability and Statistics for Engineers and Scientists, International Edition, 4e, p.71 Heckard/Utts, Statistics, International Edition, 4e, p.19/p.89 I Introduction to Probability and Its Applications, 3e, p.33 Introduction to Probability and Its Applications, International Edition, 3e, p.35 Introduction to Probability and Statistics, International Edition, 14e, p.10/p.58 Introduction to Statistics and Data Analysis, 4e, p.11/p.59 Introduction to Statistics and Data Analysis, Enhanced Review Edition (with CengageNOW Printed Access Card), 3e, p.12/p.60 Learning SAS in the Computer Lab, 3e, p.96 Lohr, Sampling, 2e, p.43 Lohr, Sampling, International Edition, 2e, p.41 M MINITAB® Handbook, International Edition, 6e, p.63 Mathematical Statistics with Applications, 7e, p.36 Mathematical Statistics with Applications, International Edition, 7e, p.39 Mendenhall/Beaver/Beaver, Introduction to Probability and Statistics, International Edition, 14e, p.10/p.58 Mind on Statistics, 2e, p.61 Mind on Statistics, 5e, p.14/p.62 O Ott/Longnecker, An Introduction to Statistical Methods and Data Analysis, 6e, p.22 Ott/Longnecker, An Introduction to Statistical Methods and Data Analysis, International Edition, 6e, p.23 P Peck, Preliminary Edition of Statistics, p.64 Peck, Statistics, p.85 Peck/Devore, Statistics, 7e, p.16/p.87 Peck/Olsen/Devore, Introduction to Statistics and Data Analysis, 4e, p.11/p.59 Peck/Olsen/Devore, Introduction to Statistics and Data Analysis, Enhanced Review Edition (with CengageNOW Printed Access Card), 3e, p.12/p.60 Pfenning, Elementary Statistics, p.2/p.48 www.cengageasia.com 99 Combined Author/Title Index Pfenning, Elementary Statistics, International Edition, p.4/p.50 Preliminary Edition of Statistics, p.64 Probability and Statistics for Engineering and the Sciences, 8e, p.67 Probability and Statistics for Engineering and the Sciences, International Edition, 8e, p.68 Probability and Statistics for Engineers and Scientists, International Edition, 4e, p.71 Probability and Statistics for Engineers, 5e, p.70 Probability and Statistics for Engineers, International Edition, 5e, p.73 Probability for Engineering, Mathematics, and Science, International Edition, p.75 Probability for Engineering, Mathematics, and Sciences, p.76 R Utts/Heckard, Mind on Statistics, 5e, p.14/p.62 Utts/Heckard/MacGillivray, Mind on Statistics, 2e, p.61 V Vining/Kowalski, Statistical Methods for Engineers, 3e, p.82 Vining/Kowalski, Statistical Methods for Engineers, International Edition, 3e, p.83 W Wackerly/Mendenhall/Scheaffer, Mathematical Statistics with Applications, 7e, p.36 Wackerly/Mendenhall/Scheaffer, Mathematical Statistics with Applications, International Edition, 7e, p.39 Rosner, Fundamentals of Biostatistics, 7e, p.56 Rosner, Fundamentals of Biostatistics, International Edition, 7e, p.57 Ryan/Joiner/Cryer, MINITAB® Handbook, International Edition, 6e, p.63 S STAT 2 (with Review Cards and CourseMate Printed Access Card), 2e, p.15/p.81 Sampling, 2e, p.43 Sampling, International Edition, 2e, p.41 Scheaffer/Mendenhall, III/Ott/Gerow, Elementary Survey Sampling, 7e, p.32 Scheaffer/Mendenhall, III/Ott/Gerow, Survey Sampling, International Edition, 7e, p.45 Scheaffer/Mulekar/McClave, Probability and Statistics for Engineers, 5e, p.70 Scheaffer/Mulekar/McClave, Probability and Statistics for Engineers, International Edition, 5e, p.73 Scheaffer/Young, Introduction to Probability and Its Applications, 3e, p.33 Scheaffer/Young, Introduction to Probability and Its Applications, International Edition, 3e, p.35 Seeing Through Statistics, 4e, p.78 Statistical Methods for Engineers, 3e, p.82 Statistical Methods for Engineers, International Edition, 3e, p.83 Statistics, p.85 Statistics with STATA, 8e, p.97 Statistics, 7e, p.16/p.87 Statistics, International Edition, 10e, p.18/p.91 Statistics, International Edition, 4e, p.19/p.89 Survey Sampling, International Edition, 7e, p.45 T Tsokos, Probability for Engineering, Mathematics, and Science, International Edition, p.75 Tsokos, Probability for Engineering, Mathematics, and Sciences, p.76 U Understandable Statistics, 11e, p.93 Understanding Basic Statistics, International Edition, 6e, p.94 Utts, Seeing Through Statistics, 4e, p.78 www.cengageasia.com 100 ORDER FORM / PRICE LIST QTY YEAR ISBN NO. AUTHOR TITLE/EDITION PAGE NO. PRICE(USD) A __ 2010 9780495017585 Ott An Introduction to Statistical Methods and Data Analysis, 6e p.22 248.95 __ 2010 9780495109143 Ott An Introduction to Statistical Methods and Data Analysis, International Edition, 6e p.23 299.95 __ 2008 9780495384984 Kleinbaum Applied Regression Analysis and Multivariable Methods, International Edition, 4e p.26 320.95 __ 2008 9780495384960 Kleinbaum Applied Regression Analysis and Other Multivariable Methods, 4e p.28 320.95 __ 2014 9781285051086 Kleinbaum Applied Regression Analysis and Other Multivariable Methods, 5e p.29 219.95 __ 2014 9781133111368 Devore Applied Statistics for Engineers and Scientists, 3e p.46 199.95 __ 2011 9780495016526 Pfenning Elementary Statistics p.2/p.48 200.95 __ 2011 9780495831457 Pfenning Elementary Statistics, International Edition p.4/p.50 200.95 __ 2012 9780538733502 Johnson Elementary Statistics, 11e p.6/p.52 276.95 __ 2008 9780495383864 Johnson Elementary Statistics, Enhanced Review Edition (w/ CD-ROM & CengageNOW, InfoTrac® 2-Sem., iLrn® Homework, Personal Tutor, Internet Companion for Statistics 2-Sem. PAC), 10e p.7/p.53 276.95 __ 2012 9780840058782 Johnson Elementary Statistics, International Edition, 11e p.9/p.55 276.95 __ 2012 9780840053619 Scheaffer Elementary Survey Sampling, 7e p.32 316.95 __ 2011 9780538733496 Rosner Fundamentals of Biostatistics, 7e p.56 226.95 __ 2011 9780538735896 Rosner Fundamentals of Biostatistics, International Edition, 7e p.57 296.95 __ 2010 9780534386719 Scheaffer Introduction to Probability and Its Applications, 3e p.33 298.95 __ 2010 9781439047262 Scheaffer Introduction to Probability and Its Applications, International Edition, 3e p.35 298.95 __ 2013 9781133111504 Mendenhall Introduction to Probability and Statistics, International Edition, 14e p.10/p.58 268.95 __ 2012 9780840054906 Peck Introduction to Statistics and Data Analysis, 4e p.11/p.59 240.95 __ 2009 9780495557838 Peck Introduction to Statistics and Data Analysis, Enhanced Review Edition (with CengageNOW Printed Access Card), 3e p.12/p.60 256.95 2010 9780495559689 Elliott Learning SAS in the Computer Lab, 3e p.96 85.95 E F I L __ M __ 2008 9780495110811 Wackerly Mathematical Statistics with Applications, 7e p.36 307.95 __ 2008 9780495385080 Wackerly Mathematical Statistics with Applications, International Edition, 7e p.39 307.95 • Prices quoted here are correct at time of printing and are subject to change without prior notice QTY YEAR ISBN NO. __ 2014 9780170227810 __ 2015 __ 2013 AUTHOR TITLE/EDITION PAGE NO. PRICE(USD) Utts Mind on Statistics, 2e p.61 162.00 9781285463186 Utts Mind on Statistics, 5e p.14/p.62 231.95 9781285175027 Ryan MINITAB® Handbook, International Edition, 6e p.63 107.95 P __ 2014 9781285049366 Peck Preliminary Edition of Statistics p.64 90.95 __ 2012 9780538733526 Devore Probability and Statistics for Engineering and the Sciences, 8e p.67 226.95 __ 2012 9780840068279 Devore Probability and Statistics for Engineering and the Sciences, International Edition, 8e p.68 307.95 __ 2013 9781133112143 Hayter Probability and Statistics for Engineers and Scientists, International Edition, 4e p.71 286.95 __ 2011 9780534403027 Scheaffer Probability and Statistics for Engineers, 5e p.70 328.95 __ 2011 9780538735902 Scheaffer Probability and Statistics for Engineers, International Edition, 5e p.73 328.95 __ 2012 9781111580735 Tsokos Probability for Engineering, Mathematics, and Science, International Edition p.76 223.95 __ 2012 9781111430276 Tsokos Probability for Engineering, Mathematics, and Sciences p.81 223.95 __ 2010 9780495105275 Lohr Sampling, 2e p.43 297.95 __ 2010 9780495110842 Lohr Sampling, 2e p.41 297.95 __ 2015 9781285050881 Utts Seeing Through Statistics, 4e p.78 166.95 __ 2012 9780538738415 Johnson STAT 2 (with Review Cards and CourseMate Printed Access Card), 2e p.15/p.81 __ 2011 9780538735186 Vining Statistical Methods for Engineers, 3e p.82 295.95 __ 2011 9780538737234 Vining Statistical Methods for Engineers, International Edition, 3e p.83 295.95 __ 2015 9780495553267 Peck Statistics p.85 142.95 __ 2013 9781111580728 Hamilton __ 2012 9780840058010 Peck __ 2012 9781111570040 Brase __ 2012 9780538735919 __ 2012 __ __ S 64.95 Statistics with STATA, 8e p.97 142.95 Statistics, 7e p.16/p.87 288.95 Statistics, International Edition, 10e p.18/p.91 271.95 Heckard Statistics, International Edition, 4e p.19/p.89 231.95 9781111569341 Scheaffer Survey Sampling, International Edition, 7e p.45 316.95 2015 9781285460918 Brase Understandable Statistics, 11e p.93 246.95 2013 9781133109983 Brase Understanding Basic Statistics, International Edition, 6e p.94 196.95 U • Prices quoted here are correct at time of printing and are subject to change without prior notice INFOGRAPHIC HOW MINDTAP TAPS INTO TECHNOLOGY TO TRANSFORM LEARNING Technology is a must in today’s higher education classroom. 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