About the Book

About the Author: 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.

Table of Contents:
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 R2: 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 Appendices Answers Credits Indexes Tables and Selected Formulas