Table of Content

PART ONE: VARIATION
1. Introduction
2. Data
3. Describing Categorical Data
4. Describing Numerical Data
5. Association between Categorical Variables
6. Association between Quantitative Variables
PART TWO: PROBABILITY
7. Probability
8. Conditional Probability
9. Random Variables
10. Association between Random Variables
11. Probability Models for Counts
12. The Normal Probability Model
PART THREE: INFERENCE
13. Samples and Surveys
14. Sampling Variation and Quality
15. Confidence Intervals
16. Statistical Tests
17. Comparison
18. Inference for Counts
PART FOUR: 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
28. Alternative Approaches to Inference 29. Regression with Big Data
30. Two-Way Analysis of Variance

In Statistics for Business: Decision Making and Analysis, authors Robert Stine and Dean Foster of the University of Pennsylvania's Wharton School, take a sophisticated approach to teaching statistics in the context of making good business decisions. The authors show students how to recognize and understand each business question, use statistical tools to do the analysis, and how to communicate their results clearly and concisely.



In addition to providing cases and real data to demonstrate real business situations, this text provides resources to support understanding and engagement. A successful problem-solving framework in the 4-M Examples (Motivation, Method, Mechanics, Message) model a clear outline for solving problems, new What Do You Think questions give students an opportunity to stop and check their understanding as they read, and new learning objectives guide students through each chapter and help them to review major goals. Software Hints provide instructions for using the most up-to-date technology packages.