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PAM 2100 – Introduction to Statistics – Spring 2017 Syllabus Professor: Tom Evans Email: [email protected] Office: 2309 MVR Telephone: 255-7010 Office hours: Monday 4:30-5; Tu Noon-3pm; W 4:30-5pm, Th 10-Noon Teaching Assist: Christine Stephan(cs2292)–Office Hrs:Tu 11-12:30pm;Th 12:30-2pm..MVR 3M48 Teaching Assist: Ishneet Kaur (ik288) Office Hours: M 4:30-6pm, W 10-11:30am…MVR 3M48 Teaching Assist: Subrina Shen (xs255) Office Hours: M & W 1-2:30pm… Uris 334 Section 201: Section 202: Section 203: Section 205: Section 206: Section 204: Th 10:10 - 11:00 Th 11:15 - 12:05 Th 12:20 - 1:10 F 10:10 - 11:00 F 11:15 - 12:05 F 1:25 - 2:15 Overview The course introduces students to standard methods of describing and analyzing data, probability theory, statistical inference, and ordinary least squares. Students will learn to describe data with summary tables and charts, understand and apply probability theory to data, understand sampling distributions, conduct hypothesis tests, estimate regressions, and interpret statistical findings. Students will also learn to use the basics of Excel to analyze data. Learning Outcomes 1. Describe large datasets using summary statistics including both central tendencies and spread. 2. Use probability theory to evaluate the expected value of future events. 3. Use inferential statistics tools to conduct hypothesis tests about proportions, means, and multiple means. 4. Estimate basic regressions using both bivariate and multivariate ordinary least squares. 5. Use Excel to conduct basic statistical, data, and graphical analysis. Course Website All course information, announcements, data sets, and so on, will be made online via Blackboard (Cornell’s system of academic websites). You should enroll in the Blackboard website “Introduction to Statistics: Spring 2017.” Course Readings The textbook for this course is “Introduction to Statistics and Data Analysis,” 5th edition, by Peck, Olsen and Devore. We will be learning the basics of Excel. Fortunately the Cornell Library has an online textbook for data analysis with Excel. The book is called “Excel 2010 for Physical Sciences Statistics.” You can either search the Cornell Library catalog, or go to this link: http://link.springer.com/book/10.1007%2F978-3-319-00630-7 There are many other Excel books available through the library in electronic form. You will receive a CISER account which will give you access to a cloud version of Excel. You can get access to Microsoft Office, and Excel, for free through this link: http://www.it.cornell.edu/services/office365/apps/student.cfm Grading Determination The following weights will be applied to determine your grade: iClicker Questions 10% Homework Assignments 20% TA Assignments 10% Prelim Exam #1 15% or 0 Prelim Exam #2 15% or 0 Final Exam 30% or 45% iClicker Questions I will present a multiple choice question at the start of most lectures (see the following schedule). The questions are to be answered with an electronic clicker that must be purchased and registered. The question will be a straightforward multiple choice question from the prior lecture. You will receive one point for simply attempting the clicker question, and an additional point if you get the question right, for a total of 2 points per question. If you miss a class you will receive zero points. I will drop your lowest 4 scores. There won’t be any make-up questions. If you are late to class and miss the clicker question you get a grade of zero. If you forget your clicker you get a grade of zero. If you lose or break your clicker, or the batteries run low, you get a grade of zero. Homework Assignments I will assign 5 standard homework assignments. First, the assignments will be representative of the types of questions you can expect to get on the prelims and final exam. Additionally, I will use the assignments to force you to use Excel to answer statistics and data analysis questions. I will drop your lowest homework grade. The remaining assignments will be equally weighted and count 5% each towards your final grade. There will be no make up assignments or extensions for assignments. If you miss an assignment you will simply get a grade of zero and presumably that will be the grade that is dropped. TA Assignments Due to the size of the class the format of the class will be predominately lecture. However, there are six TA sections of approximately 25 students each. In the section you will be able to ask questions. The TA will also go over practice problems and help you with learning Excel. Finally, during most weeks there will be problems that you will need to complete during the course of the section. Additionally, occasionally I will hand out short assignments to be completed during regular class times. These problems will be graded. 10% of your grade will be based on the TA assignments or in class assignments. I will drop your three lowest grades. Exams There will be two preliminary and one final exam. All exams will be closed book and closed notes (I will provide a page of formulae). The preliminary exams will be held in class during regular class time. The sum of the mid-terms and final will count for 60% of your final grade. If your final exam grade is lower than both midterms, the final exam will count for 30% of your grade and the midterms will count 15% each. However, if your final exam grade is greater than either one of your preliminary exam grades, your final exam will count for 45% of your final grade and your lowest preliminary exam will be dropped. As a result, there won’t be any make up prelim exams. If you miss a prelim your score of zero will simply be dropped and the final exam will count for 45% of your final grade. Regrading Procedures While we certainly aim to eliminate grading errors, we are only human. If you believe that there is an error on one of your submissions you will have one week after it has been handed back to petition for a regrade. I will have the graders come to the end of class and you can discuss any error you think has been made. If you are not satisfied with the graders response, I will stay and you can then appeal to me. There will be one specific time for each assignment; you will not be permitted to appeal your grade on an assignment afterwards. Increasing you grades though because “you need it’ is out of the questions so please only request regrades when you think an honest mistake has been made on our part. Office Hours/Getting Help Due to the size of the class it will be impossible for all of you to make my office hours a productive forum for you to ask questions. However, I will hold office hours for you if you need to discuss personal issues or need to bring up problems with the class. The two TA’s will be holding weekly office hours where you can get individual help on the course material, or if you need help with the research project. You can go to either of the TA office hours that are most convenient to you. Academic Integrity Each student in the course is expected to abide by the Cornell University Code of Academic Integrity: (see http://cuinfo.cornell.edu/Academic/AIC.html.) Any work submitted by a student in this course for academic credit must be the student’s own work. Disability Accommodations If you have a disability that requires accommodation, especially additional time for exams, please bring a copy of your approval letter to my attention as early as possible in the semester so that I can make arrangements. Outside Learning Resources For students who are having difficulty in the class here are two suggested outside resources: 1) Cornell operates the Learning Strategies Center for statistics. Here is the link: http://lsc.cornell.edu/Sidebars/statistics_lab.html 2) Online videos from Khan Academy: https://www.khanacademy.org/math/probability Class Schedule: W Jan 25 First day of class M W Jan 30 Feb 1 Practice iClicker iC1 M W Feb 6 Feb 8 iC2 iC3 M W Feb 13 Feb 15 iC4 Homework 1 M W Feb 20 Feb 22 February Break…no class iC5 M W Feb 27 Mar 1 iC6 iC7 M W Mar 6 Mar 8 iC8 Homework 2 M W Mar 13 Mar 15 Prelim 1 iC9 M W Mar 20 Mar 22 iC10 iC11 M W Mar 27 Mar 29 iC12 Homework 3 M W M W Apr 3 Apr 5 Apr 10 Apr 12 Spring Break…no class Spring Break…no class iC13 iC14 M W Apr 17 Apr 19 iC15 Homework 4 M W Apr 24 Apr 26 Prelim 2 iC16 M W May 1 May 3 iC17 iC18 M W May 8 May 10 Homework 5 Last Day of Classes Final Exam: To be determined – at the University scheduled time and place. Order of Topics Chapter 1: Introduction 1.1 Why Study Statistics? 1.2 The Nature and Role of Variability 1.3 Statistics and the Data Analysis Process 1.4 Types of Data and Some Simple Graphical Displays Chapter 3: Graphical Methods for Describing Data 3.1 Displaying Categorical Data: Comparative Bar Charts and Pie Charts 3.3 Displaying Numerical Data: Frequency Distributions and Histograms 3.4 Displaying Bivariate Numerical Data 3.5 Interpreting and Communicating the Results of Statistical Analyses Chapter 4: Numerical Methods for Describing Data 4.1 Describing the Center of a Data Set 4.2 Describing Variability in a Data Set 4.3 Summarizing a Data Set: Boxplots 4.4 Interpreting Center and Variability: Chebyshev’s Rule, the Empirical Rule, and z Scores 4.5 Interpreting and Communicating the Results of Statistical Analyses Percent and percentage point Ratio vs fraction Chapter 6: Probability 6.1 Chance Experiments and Events 6.2 Definition of Probability 6.3 Basic Properties of Probability 6.4 Conditional Probability 6.5 Independence 6.6 Some General Probability Rules 6.7 Estimating Probabilities Empirically Using Simulation Bayes Theorem Counting rules Expected Value Chapter 7: Random Variables and Probability Distributions 7.1 Random Variables 7.2 Probability Distributions for Discrete Random Variables 7.3 Probability Distributions for Continuous Random Variables 7.4 Mean and Standard Deviation of a Random Variable 7.5 Binomial and Geometric Distributions 7.6 Normal Distributions 7.8 Using the Normal Distribution to Approximate a Discrete Distribution Expected value again Chapter 8: Sampling Variability and Sampling Distributions 8.1 Statistics and Sampling Variability 8.2 The Sampling Distribution of a Sample Mean 8.3 The Sampling Distribution of a Sample Proportion Chapter 2: Collecting Data Sensibly 2.1 Statistical Studies 2.2 Sampling 2.3 Simple Comparative Experiments 2.4 More on Experimental Design Chapter 9: Estimation Using a Single Sample 9.1 Point Estimation 9.2 Large-Sample Confidence Interval for a Population Proportion 9.3 Confidence Interval for a Population Mean 9.4 Interpreting and Communicating the Results of Statistical Analyses Chapter 10: Hypothesis Testing Using a Single Sample 10.1 Hypotheses and Test Procedures 10.2 Errors in Hypothesis Testing 10.3 Large-Sample Hypothesis Tests for a Population Proportion 10.4 Hypothesis Tests for a Population Mean 10.5 Power and Probability of Type II Error 10.6 Interpreting and Communicating the Results of Statistical Analyses Chapter 11: Comparing Two Populations or Treatments 11.1Inferences Concerning the Difference between Two Population or Treatment Means Using Independent Samples 11.2Inferences Concerning the Difference between Two Population or Treatment Means Using Paired Samples 11.3Large-Sample Inferences Concerning the Difference between Two Population or Treatment Proportions 11.4Interpreting and Communicating the Results of Statistical Analyses Chapter 5: Summarizing Bivariate Data 5.1 Correlation 5.2 Linear Regression: Fitting a Line to Bivariate Data 5.3 Assessing the Fit of a Line Chapter 13: Simple Linear Regression and Correlation: Inference 13.1 Simple Linear Regression Model 13.2 Inferences About the Slope of the Population Regression Line 13.3 Checking Model Adequacy 13.4 Inferences Based on the Estimated Regression Line (Optional) 13.5 Inferences About the Population Correlation Coefficient (Optional) Chapter 14: Multiple Regression Analysis 14.1 Multiple Regression Models