Robert Stine holds a Ph.D. from Princeton University. He has taught at the Wharton School since 1983, during which time he has regularly taught business  statistics. During his tenure, Bob has received a variety of teaching awards, including regularly winning the MBA Core Teaching Award, which is presented to faculty for outstanding teaching of the required curriculum at Wharton. He also received the David W. Hauck Award for Outstanding Teaching, awarded to the most highly rated faculty member teaching in the Wharton undergraduate program. Bob actively consults for industry. His clients include the pharmaceutical firms Merck and Pfizer, and he regularly works with the Federal Reserve Bank of Philadelphia on models for retail credit risk. This collaboration has produced three well-received conferences held at Wharton. His areas of research include computer software, time series analysis and forecasting, and general problems related to model identification and selection. Bob has published numerous articles in research journals, including the Journal of the American Statistical Association, Journal of the Royal Statistical Society, Biometrika, and The Annals of Statistics.

Dean Foster holds a Ph.D. from the University of Maryland. He has taught at the Wharton School since 1992 and previously taught at the University of Chicago. Dean teaches courses in introductory business statistics, probability and  Markov chains, statistical computing and advanced statistics for managers. Dean’s research areas are statistical inference for stochastic processes, game theory, machine learning, and variable selection. He is published in a wide variety of journals, including The Annals of Statistics, Operations Research, Games and Economic Behaviour, Journal of Theoretical Population Biology, and Econometrica. Dean is currently Senior Principal Scientist at Amazon, working on big data, with the goal of predicting the sales of each and every item that Amazon sells.

I. Variation

1. Introduction

2. Data

3. Describing Categorical Data

4. Describing Numerical Data

5. Association Between Categorical Variables

6. Association Between Quantitative Variables

II. Probability

7. Probability

8. Conditional Probability

9. Random Variables

10. Association Between Random Variables

11. Probability Models for Counts

12. The Normal Probability Model

III. Inference

13. Samples and Surveys

14. Sampling Variation and Quality

15. Confidence Intervals

16. Statistical Tests

17. Comparison

18. Inference for Counts

IV. Regression Models

19. Linear Patterns

20. Curved Patterns

21. The Simple Regression Model

22. Regression Diagnostics

23. Multiple Regression

24. Building Regression Models

25. Categorical Explanatory Variables

26. Analysis of Variance

27. Time Series

Supplementary Material (Online-Only)

S1 Alternative Approaches to Inference

S2 Two-Way Analysis of Variance

S3 Regression with Big Data

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