Richard D. De Veaux is an internationally known educator and consultant. He has taught at the Wharton School and the Princeton University School of Engineering, where he won a “Lifetime Award for Dedication and Excellence in Teaching.” He is the C. Carlisle and M. Tippit Professor of Statistics at Williams College, where he has taught since 1994. Dick has won both the Wilcoxon and Shewell awards from the American Society for Quality. He is a fellow of the American Statistical Association (ASA) and an elected member of the International Statistical Institute (ISI). In 2008, he was named Statistician of the Year by the Boston Chapter of the ASA. Dick is also well known in industry, where for more than 30 years he has consulted for such Fortune 500 companies as American Express, Hewlett-Packard, Alcoa, DuPont, Pillsbury, General Electric, and Chemical Bank. Because he consulted with Mickey Hart on his book Planet Drum, he has also sometimes been called the “Official Statistician for the Grateful Dead.” His real-world experiences and anecdotes illustrate many of this book’s chapters.

Dick holds degrees from Princeton University in Civil Engineering (B.S.E.) and Mathematics (A.B.) and from Stanford University in Dance Education (M.A.) and Statistics (Ph.D.), where he studied dance with Inga Weiss and Statistics with Persi Diaconis. His research focuses on the analysis of large data sets and data mining in science and industry.

In his spare time, he is an avid cyclist and swimmer. He also is the founder of the “Diminished Faculty,” an a cappella Doo-Wop quartet at Williams College, and sings bass in the college concert choir and with the Choeur Vittoria of Paris. Dick is the father of four children.

Paul F. Velleman has an international reputation for innovative Statistics education. He is the author and designer of the multimedia Statistics program ActivStats, for which he was awarded the EDUCOM Medal for innovative uses of computers in teaching statistics, and the ICTCM Award for Innovation in Using Technology in College Mathematics. He also developed the award-winning statistics program Data Desk, and the Internet site Data and Story Library (DASL) (ASL.datadesk.com), which provides data sets for teaching Statistics. Paul’s understanding of using and teaching with technology informs much of this book’s approach.

Paul has taught Statistics at Cornell University since 1975, where he was awarded the MacIntyre Award for Exemplary Teaching. He holds an A.B. from Dartmouth College in Mathematics and Social Science, and M.S. and Ph.D. degrees in Statistics from Princeton University, where he studied with John Tukey. His research often deals with statistical graphics and data analysis methods. Paul co-authored (with David Hoaglin) ABCs of Exploratory Data Analysis. Paul is a Fellow of the American Statistical Association and of the American Association for the Advancement of Science. Paul is the father of two boys.

David E. Bock taught mathematics at Ithaca High School for 35 years. He has taught Statistics at Ithaca High School, Tompkins-Cortland Community College, Ithaca College, and Cornell University. Dave has won numerous teaching awards, including the MAA’s Edyth May Sliffe Award for Distinguished High School Mathematics Teaching (twice), Cornell University’s Outstanding Educator Award (three times), and has been a finalist for New York State Teacher of the Year.

Dave holds degrees from the University at Albany in Mathematics (B.A.) and Statistics/Education (M.S.). Dave has been a reader and table leader for the AP Statistics exam, serves as a Statistics consultant to the College Board, and leads workshops and institutes for AP Statistics teachers. He has served as K–12 Education and Outreach Coordinator and a senior lecturer for the Mathematics Department at Cornell University. His understanding of how students learn informs much of this book’s approach.

Dave and his wife relax by biking or hiking, spending much of their free time in Canada, the Rockies, or the Blue Ridge Mountains. They have a son, a daughter, and four grandchildren.

Preface

Index of Applications

I: EXPLORING AND UNDERSTANDING DATA

1. Stats Starts Here 

1.1 What Is Statistics?  1.2 Data  1.3 Variables  1.4 Models

2. Displaying and Describing Data

2.1 Summarizing and Displaying a Categorical Variable  2.2 Displaying a Quantitative Variable  2.3 Shape  2.4 Center  2.5 Spread 

3. Relationships Between Categorical Variables–Contingency Tables

3.1 Contingency Tables  3.2 Conditional Distributions  3.3 Displaying Contingency Tables  3.4 Three Categorical Variables

4. Understanding and Comparing Distributions

4.1 Displays for Comparing Groups  4.2 Outliers  4.3 Re-Expressing Data: A First Look

5. The Standard Deviation as a Ruler and the Normal Model

5.1 Using the Standard Deviation to Standardize Values  5.2 Shifting and Scaling  5.3 Normal Models  5.4 Working with Normal Percentiles  5.5 Normal Probability Plots

Review of Part I: Exploring and Understanding Data

II. EXPLORING RELATIONSHIPS BETWEEN VARIABLES

6. Scatterplots, Association, and Correlation

6.1 Scatterplots 6.2 Correlation 6.3 Warning: Correlation ≠ Causation *6.4 Straightening Scatterplots

7. Linear Regression

7.1 Least Squares: The Line of “Best Fit” 7.2 The Linear Model 7.3 Finding the Least Squares Line 7.4 Regression to the Mean 7.5 Examining the Residuals 7.6 R ²–The Variation Accounted for by the Model  7.7 Regression Assumptions and Conditions

8. Regression Wisdom

8.1 Examining Residuals  8.2 Extrapolation: Reaching Beyond the Data  8.3 Outliers, Leverage, and Influence  8.4 Lurking Variables and Causation  8.5 Working with Summary Values  *8.6 Straightening Scatterplots–The Three Goals  *8.7 Finding a Good Re-Expression

9. Multiple Regression

9.1 What Is Multiple Regression?  9.2 Interpreting Multiple Regression Coefficients  9.3 The Multiple Regression Model–Assumptions and Conditions  9.4 Partial Regression Plots  *9.5 Indicator Variables 

Review of Part II: Exploring Relationships Between Variables 

III. GATHERING DATA

10. Sample Surveys

10.1 The Three Big Ideas of Sampling  10.2 Populations and Parameters  10.3 Simple Random Samples  10.4 Other Sampling Designs  10.5 From the Population to the Sample: You Can’t Always Get What You Want  10.6 The Valid Survey 10.7 Common Sampling Mistakes, or How to Sample Badly

11. Experiments and Observational Studies

11.1  Observational Studies  11.2 Randomized, Comparative Experiments  11.3 The Four Principles of Experimental Design 11.4 Control Groups  11.5 Blocking  11.6 Confounding

Review of Part III: Gathering Data

IV. RANDOMNESS AND PROBABILITY 

12. From Randomness to Probability

12.1 Random Phenomena  12.2 Modeling Probability  12.3 Formal Probability

13.Probability Rules!

13.1 The General Addition Rule  13.2 Conditional Probability and the General Multiplication Rule  13.3 Independence  13.4 Picturing Probability: Tables, Venn Diagrams, and Trees  13.5 Reversing the Conditioning and Bayes’ Rule

14. Random Variables

14.1 Center: The Expected Value  14.2 Spread: The Standard Deviation  14.3 Shifting and Combining Random Variables  14.4 Continuous Random Variables

15. Probability Models

15.1 Bernoulli Trials  15.2 The Geometric Model  15.3 The Binomial Model  15.4 Approximating the Binomial with a Normal Model  15.5 The Continuity Correction  15.6 The Poisson Model  15.7 Other Continuous Random Variables: The Uniform and the Exponential

Review of Part IV: Randomness and Probability

V. INFERENCE FOR ONE PARAMETER 

16. Sampling Distribution Models and Confidence Intervals for Proportions

16.1 The Sampling Distribution Model for a Proportion  16.2 When Does the Normal Model Work? Assumptions and Conditions  16.3 A Confidence Interval for a Proportion  16.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean? 16.5 Margin of Error: Certainty vs. Precision  *16.6 Choosing the Sample Size

17. Confidence Intervals for Means

17.1 The Central Limit Theorem  17.2 A Confidence Interval for the Mean  17.3 Interpreting Confidence Intervals  *17.4 Picking Our Interval up by Our Bootstraps  17.5 Thoughts About Confidence Intervals

18. Testing Hypotheses

18.1 Hypotheses 18.2 P-Values  18.3 The Reasoning of Hypothesis Testing  18.4 A Hypothesis Test for the Mean  18.5 Intervals and Tests  18.6 P-Values and Decisions: What to Tell About a Hypothesis Test

19. More About Tests and Intervals

19.1 Interpreting P-Values  19.2 Alpha Levels and Critical Values  19.3 Practical vs. Statistical Significance  19.4 Errors

Review of Part V: Inference for One Parameter

VI. INFERENCE FOR RELATIONSHIPS

20. Comparing Groups

20.1 A Confidence Interval for the Difference Between Two Proportions  20.2 Assumptions and Conditions for Comparing Proportions  20.3 The Two-Sample z-Test: Testing for the Difference Between Proportions 20.4 A Confidence Interval for the Difference Between Two Means 20.5 The Two-Sample t-Test: Testing for the Difference Between Two Means *20.6 Randomization Tests and Confidence Intervals for Two Means *20.7 Pooling  *20.8 The Standard Deviation of a Difference 

21. Paired Samples and Blocks

21.1 Paired Data  21.2 The Paired t-Test  21.3 Confidence Intervals for Matched Pairs  21.4 Blocking

22. Comparing Counts

22.1 Goodness-of-Fit Tests  22.2 Chi-Square Test of Homogeneity  22.3 Examining the Residuals  22.4 Chi-Square Test of Independence 

23. Inferences for Regression

23.1 The Regression Model  23.2 Assumptions and Conditions  23.3 Regression Inference and Intuition  23.4 The Regression Table  23.5 Multiple Regression Inference  23.6 Confidence and Prediction Intervals  *23.7 Logistic Regression  *23.8 More About Regression

Review of Part VI: Inference for Relationships

VII. INFERENCE WHEN VARIABLES ARE RELATED

24. Multiple Regression Wisdom

24.1 Multiple Regression Inference  24.2 Comparing Multiple Regression Model  24.3 Indicators  24.4 Diagnosing Regression Models: Looking at the Cases  24.5 Building Multiple Regression Models

25. Analysis of Variance

25.1 Testing Whether the Means of Several Groups Are Equal  25.2 The ANOVA Table  25.3 Assumptions and Conditions  25.4 Comparing Means  25.5 ANOVA on Observational Data

26. Multifactor Analysis of Variance

26.1 A Two Factor ANOVA Model   26.2 Assumptions and Conditions  26.3 Interactions

27. Statistics and Data Science

27.1 Introduction to Data Mining

Review of Part VII: Inference When Variables Are Related

Parts I—V Cumulative Review Exercises

Appendixes:

A. Answers 

B. Credits 

C. Indexes 

D. Tables and Selected Formulas 

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