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THE CITY COLLEGE Department of Computer Science MIS G1010. Statistics and Decision Making Fall 2015 Prof. Abbe Mowshowitz Office: NAC 7/244 Phone: (212) 650-6161 Hours: M: 4-6 email: [email protected] TEXT: Sharpe, De Veaux, and Velleman. Business Statistics (second edition). Addison-Wesley, 2012. LECTURE NOTES: www-cs.ccny.cuny.edu/~abbe/BS GRADING: Test #1 (20%), Test #2 (20%), assignments (20%), final exam-cumulative (40%). The objective of this course is to help you learn to analyze data and use methods of statistical inference in making business decisions. Central to the course is the application of fundamental concepts covered in probability and decision making to the problem of drawing inferences from data on observed outcomes. Topics covered during the first part of the course will include statistical sampling and sampling distributions, point estimation and confidence intervals, hypothesis testing, and correlations among variables. The second part of the course will focus on multivariate analysis, with special attention paid to the inferences that may drawn with respect to prediction and causality. ASSIGNMENTS: (1) Mini case study projects for class presentation and discussion. Each week a student will be responsible for analyzing and leading a discussion of one of the mini case studies in the textbook. (2) Problem assignments: five exercises from the chapters covered in the text will be assigned every two weeks and the solutions will be discussed in class. Course Outline Week 1. Foundations 1: Variation, Data, Surveys and Sampling (Chapters 1-3) Overview; data in statistical analysis; surveys and sampling. Week 2. Foundations 2: Displaying and Describing Data, Correlation (Chapters 4-6) Frequency tables, charts and contingency tables; elementary probability theory; quantitative data: boxplots, outliers, standardization. Week 3. Randomness and Probability Models (Chapter 7-8) Expected value, variance and standard deviation of a random variable; discrete probability models; continuous, random variables. Week 4. Normal Model (Chapter 9) Standard deviation; normal distribution; plot; sums of normal; approximation. 1 Week 5. Sampling Distributions and the Normal Model (Chapter 10) Disribution of sample proportions; sampling distribution for proportions; Central Limit Theorem; sampling distribution of the mean. ** Test #1 (October 5 – Week 5) – one hour covering Weeks 1-4 Week 6. Confidence Intervals for Proportions, Confidence Intervals for Means (Chaps. 11-12) Confidence intervals; margin of error; sample size; sampling distribution for the mean; confidence interval for means; degrees of freedom. Week 7. Testing Hypotheses (Chapter 13) Hypotheses; trial as hypothesis test; P-values; alternative hypotheses; one-sample t-test; alpha levels and significance; critical values; confidence intervals and tests; types of error. Week 8. Comparing Two Groups (Chapter 14) Comparing two means; two-sample t-test; confidence interval for difference between means; Paired data; pooled t-test; paired t-test. Week 9. Inference for Counts: Chi-Square Tests (Chapter 15) Goodness of fit tests; interpreting Chi-square values; analyzing residuals; Chi-square tests for homogeneity and independence. ** Test #2 (Nov. 9 – Week 9) – one hour covering Weeks 5-8 Week 10. Inference for Regression (Chapter 16) Population and sample; standard error of slope; test for the regression slope; hypothesis test for correlation; standard errors. Week 11. Multiple Regression (Chapter 18) Multiple regression model; multiple regression coefficients; assumptions and conditions; testing the model. Week 12. Time Series Analysis (Chapter 20) Components of time series; smoothing methods; simple and weighted moving averages; exponential smoothing; autoregressive models; random walks; forecasting with regression models. Weeks 13-14. Design and Analysis of Experiments and Observational Studies (Chapter 21) Observational studies; experimental design; one-way analysis of variance (ANOVA); assumptions and conditions; multifactor designs. 2