Statistics for Business

Robert Stine holds a PhD 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. Bob also 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. He was recently awarded the 2011 Helen Kardon Moss Anvil Award for outstanding teaching quality at the Wharton School.   Dean Foster holds a PhD 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, andEconometrica.   Bob Stine and Dean Foster have co-authored two casebooks: Basic Business Statistics (Springer-Verlag) and Business Analysis Using Regression (Springer-Verlag). These casebooks offer a collection of data analysis examples that motivate and illustrate key ideas of statistics, ranging from standard error to regression diagnostics and time series analysis. They also have collaborated on a number of research articles.

PART 1: VARIATION IN DATA

1. Introduction

1.1 What is Statistics?

1.2 Previews

1.3 How to Use This Book

 

2. Data

2.1 Data Tables

2.2 Categorical and Numerical Data

2.3 Recoding and Aggregation

2.4 Time Series

2.5 Further Attributes of Data

 

3. Describing categorical data

3.1 Looking at Data

3.2 Charts of Categorical Data

3.3 The Area Principle

3.4 Mode and Median

 

4. Describing numerical data

4.1 Summaries of Numerical Variables

4.2 Histograms and the Distribution of Numerical Data

4.3 Boxplot

4.4 Shape of a Distribution

 

5. Association in categorical data

5.1 Contingency Tables

5.2 Lurking Variables and Simpson's Paradox

5.3 Strength of Association

 

6. Association in numerical data

6.1 Scatterplots

6.2 Association in Scatterplots

6.3 Measuring Association

6.4 Summarizing Association with a Line

6.5 Spurious Correlation

 

Statistics in Action: Financial time series

Statistics in Action: Executive compensation

 

PART 2: PROBABILITY

7. Probability

7.1 From Data to Probability

7.2 Rules for Probability

7.3 Independent Events

7.4 Boole's Inequality

 

8. Conditional Probability

8.1 From Tables to Probabilities

8.2 Dependent Events

8.3 Organizing Probabilities

8.4 Order in Conditional Probabilities

 

9. Random Variables

9.1 Properties of Random Variables

9.2 Expected Values

9.3 Comparing Random Variables

 

10. Association between Random Variables

10.1 Portfolios and Random Variables

10.2 Probability Distribution

10.3 Sums of Random Variables

10.4 Measure Dependence between Random Variables

10.5 IID Random Variables

 

11. Probability models for Counts

11.1 Random Variables for Counts

11.2 Binomial Model

11.3 Properties of Binomial Random Variables

11.4 Poisson Model

 

12. Normality

12.1 Normal Random Variable

12.2 The Normal Model

12.3 Percentiles of the Normal Distribution

12.4 Departures from Normality

 

Statistics in Action: Managing Financial Risk

Statistics in Action: Modeling Sampling Variation

 

PART 3: INFERENCE

13. Samples and Surveys

13.1 Two Surprises in Sampling

13.2 Variation

13.3 Alternative Sampling Methods

13.4 Checklist for Surveys

 

14. Sampling Variation and Quality

14.1 Sampling Distribution of the Mean

14.2 Control Limits

14.3 Using a Control Chart

14.4 Control Charts for Variation

 

15. Confidence Intervals

15.1 Ranges for Parameters

15.2 Confidence Interval for the Mean

15.3 Interpreting Confidence Intervals

15.4 Manipulating Confidence Intervals

15.5 Margin of Error

 

16. Statistical Tests

16.1 Concepts of Statistical Tests

16.2 Testing the Proportion

16.3 Testing the Mean

16.4 Other Properties of Tests

 

17. Alternative Approaches to Inference

17.1 A Confidence Interval for the Median

17.2 Transformations and Intervals

17.3 Prediction Intervals

17.4 Proportions Based on Small Samples

 

18. Comparison

18.1 Data for Comparisons

18.2 Two-sample T-test

18.3 Confidence Interval for the Difference

18.4 Other Comparisons

 

Statistics in Action: Rare Events

Statistics in Action: Testing Association

 

PART 4: REGRESSION MODELS

19. Linear Patterns

19.1 Fitting a Line to Data

19.2 Interpreting the Fitted Line

19.3 Properties of Residuals

19.4 Explaining Variation

19.5 Conditions for a Simple Regression

 

20. Curved Patterns

20.1 Detecting Nonlinear Patterns

20.2 Reciprocal Transformation

20.3 Comparing a Linear and Nonlinear Equation

20.4 Logarithm Transformation

20.5 Comparing Equations

 

21. Simple Regression

21.1 The Simple Regression Model

21.2 Conditions for the Simple Regression Model

21.3 Inference in Regression

21.4 Prediction Intervals

 

22. Regression Diagnostics

22.1 Changing Variation

22.2 Leveraged Outliers

22.3 Dependent Errors and Time Series

 

23. Multiple Regression

23.1 The Multiple Regression Model

23.2 Interpreting Multiple Regression

23.3 Checking Conditions

23.4 Inference in Multiple Regression

23.5 Steps in Building a Multiple Regression

 

24. Building Regression Models

24.1 Identifying Explanatory Variables

24.2 Collinearity

24.3 Removing Explanatory Variables

 

25. Categorical Explanatory Variables

25.1 Two-sample Comparisons

25.2 Analysis of Covariance

25.3 Checking Conditions

25.4 Interactions and Inference

25.5 Regression with Several Groups

 

26. Analysis of Variance

26.1 Comparing Several Groups

26.2 Inference in Anova Regression Models

26.3 Multiple Comparisons

26.4 Groups of Different Size

 

27. Time Series

27.1 Decomposing a Time Series

27.2 Regression Models

27.3 Checking Conditions

 

Statistics in Action: Analyzing Experiments

Statistics in Action: Automated Regression Modeling