Intro Stats, Global Edition

About our authors 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, and was the 2018-2021 Vice-President 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 4 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, the Internet site Data and Story Library (DASL) which provides data sets for teaching Statistics, and the tools referenced in the text for simulation and bootstrapping. Paul's understanding of using and teaching with technology informs much of this book's approach.Paul taught Statistics at Cornell University, where he was awarded the MacIntyre Award for Exemplary Teaching. He is Emeritus Professor of Statistical Science from Cornell and lives in Maine with his wife, Sue Michlovitz. 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 2 boys. In his spare time he sings with the acapella group VoXX and studies tai chi.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 (3 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 and a Statistics consultant to the College Board, leading workshops and institutes for AP Statistics teachers. His understanding of how students learn informs much of this book's approach.

• * Indicates optional sectionI: EXPLORING AND UNDERSTANDING DATA Stats Starts Here 1.1 What Is Statistics?1.2 Data1.3 Variables1.4 ModelsDisplaying and Describing Data 2.1 Summarizing and Displaying a Categorical Variable2.2 Displaying a Quantitative Variable2.3 Shape2.4 Center2.5 SpreadRelationships Between Categorical Variables: Contingency Tables 3.1 Contingency Tables3.2 Conditional Distributions3.3 Displaying Contingency Tables3.4 Three Categorical VariablesUnderstanding and Comparing Distributions 4.1 Displays for Comparing Groups4.2 Outliers4.3 Re-Expressing Data: A First LookThe Standard Deviation as a Ruler and the Normal Model 5.1 Using the standard deviation to Standardize Values5.2 Shifting and Scaling5.3 Normal Models5.4 Working with Normal Percentiles5.5 Normal Probability Plots Review of Part I: Exploring and Understanding DataII: EXPLORING RELATIONSHIPS BETWEEN VARIABLES Scatterplots, Association, and Correlation 6.1 Scatterplots6.2 Correlation6.3 Warning: Correlation ≠ Causation6.4 *Straightening ScatterplotsLinear Regression 7.1 Least Squares: The Line of "Best Fit"7.2 The Linear Model7.3 Finding the Least Squares Line7.4 Regression to the Mean7.5 Examining the Residuals7.6 R2: The Variation Accounted for by the Model7.7 Regression Assumptions and ConditionsRegression Wisdom 8.1 Examining Residuals8.2 Extrapolation: Reaching Beyond the Data8.3 Outliers, Leverage, and Influence8.4 Lurking Variables and Causation8.5 Working with Summary Values8.6 * Straightening Scatterplots: The Three Goals8.7 * Finding a Good Re-ExpressionMultiple Regression 9.1 What Is Multiple Regression?9.2 Interpreting Multiple Regression Coefficients9.3 The Multiple Regression Model: Assumptions and Conditions9.4 Partial Regression Plots9.5 * Indicator Variables Review of Part II: Exploring Relationships Between VariablesIII: GATHERING DATA Sample Surveys 10.1 The Three Big Ideas of Sampling10.2 Populations and Parameters10.3 Simple Random Samples10.4 Other Sampling Designs10.5 From the Population to the Sample: You Can't Always Get What You Want10.6 The Valid Survey10.7 Common Sampling Mistakes, or How to Sample BadlyExperiments and Observational Studies 11.1 Observational Studies11.2 Randomized, Comparative Experiments11.3 The Four Principles of Experimental Design11.4 Control Groups11.5 Blocking11.6 Confounding Review of Part III: Gathering DataIV: FROM THE DATA AT HAND TO THE WORLD AT LARGE From Randomness to Probability 12.1 Random Phenomena12.2 Modeling Probability12.3 Formal Probability12.4 Conditional Probability and the General Multiplication Rule12.5 Independence12.6 Picturing Probability: Tables, Venn Diagrams, and Trees12.7 Reversing the Conditioning and Bayes' RuleSampling Distributions and Confidence Intervals for Proportions 13.1 The Sampling Distribution for a Proportion13.2 When Does the Normal Model Work? Assumptions and Conditions13.3 A Confidence Interval for a Proportion13.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?13.5 Margin of Error: Certainty vs. Precision13.6 * Choosing the Sample SizeConfidence Intervals for Means 14.1 The Central Limit Theorem14.2 A Confidence interval for the Mean14.3 Interpreting confidence intervals14.4 * Picking our Interval Up by our Bootstraps14.5 Thoughts about Confidence IntervalsTesting Hypotheses 15.1 Hypotheses15.2 P-values15.3 The Reasoning of Hypothesis Testing15.4 A Hypothesis Test for the Mean15.5 Intervals and Tests15.6 P-Values and Decisions: What to Tell About a Hypothesis TestMore About Tests and Intervals 16.1 Interpreting P-values16.2 Alpha Levels and Critical Values16.3 Practical vs. Statistical Significance16.4 Errors Review of Part IV: From the Data at Hand to the World at LargeV: INFERENCE FOR RELATIONSHIPS Comparing Groups 17.1 A Confidence Interval for the Difference Between Two Proportions17.2 Assumptions and Conditions for Comparing Proportions17.3 The Two-Sample z-Test: Testing the Difference Between Proportions17.4 A Confidence Interval for the Difference Between Two Means17.5 The Two-Sample t-Test: Testing for the Difference Between Two Means17.6 * Randomization-Based Tests and Confidence Intervals for Two Means17.7 * Pooling17.8 * The Standard Deviation of a DifferencePaired Samples and Blocks 18.1 Paired Data18.2 The Paired t-Test18.3 Confidence Intervals for Matched Pairs18.4 BlockingComparing Counts 19.1 Goodness-of-Fit Tests19.2 Chi-Square Tests of Homogeneity19.3 Examining the Residuals19.4 Chi-Square Test of IndependenceInferences for Regression 20.1 The Regression Model20.2 Assumptions and Conditions20.3 Regression Inference and Intuition20.4 The Regression Table20.5 Multiple Regression Inference20.6 Confidence and Prediction Intervals20.7 * Logistic Regression20.8 * More About Regression Review of Part V: Inference for RelationshipsParts I–V Cumulative Review Exercises Appendixes: AnswersCreditsIndexesTables and Selected Formulas