Synopsis
Class 1. Fitting Equations to Data.- Efficiency of Cleaning Crews.- Liquor Sales and Display Space.- Managing Benefits Costs.- Predicting Cellular Phone use.- Class 2. Assumptions in Regression Modeling.- The Ideal Regression Model.- Predicting Cellular Phone use, Revisited.- Efficiency of Cleaning Crews, Revisited.- Housing Prices and Crime Rates.- Direct Mail Advertising and Sales.- Housing Construction.- Class 3. Prediction and Confidence Intervals in Regression.- Housing Construction, Revisited.- Liquor Sales and Display Space, Revisited.- Class 4. Multiple Regression.- Automobile Design.- Class 5. Collinearity.- Stock Prices and Market Indices.- Improving Parcel Handling.- Class 6. Modeling Categorical Factors with two Levels.- Employee Performance Study.- Class 7. Modeling Categorical Factors with two or More Levels.- Wage Discrimination, Revisited.- Timing Production Runs.- Class 8. Summary Regression Case.- Executive Compensation.- Using Stepwise Regression for Prediction.- Class 9. Comparing Many Mean Values.- Selecting the Best Vendor.- Headache Pain Relief.- Analysis of Variance and Tests for Linearity.- Class 10. Analysis of Variance with Two Factors.- Package Design Experiment.- Evaluating Employee time Schedules.- Class 11. Modeling a Categorical Response.- The Challenger Disaster.- Marketing Orange Juice.- Class 12. Modeling Time Series.- Predicting Cellular Phone use, Revisited.- Trends in Computer Sales.- Assignments.- Appendix: Use with Minitab.
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Product Description
This book provides a set of case studies that can supplement any text on regression analysis. Developed at the Wharton School of the University of Pennsylvania, it is used for the second semester of a beginning statistics course for MBA students. The casebook provides the latest in data analysis techniques, and includes a discussion of JMP and Minitab commands.
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