I: EXPLORING AND UNDERSTANDING DATA 1. Stats Starts Here 1.1 What Is Statistics?1.2 Data1.3 Variables1.4 Models 2. Displaying and Describing Data 2.1 Summarizing and Displaying a Categorical Variable2.2 Displaying a Quantitative Variable2.3 Shape2.4 Center2.5 Spread 3. Relationships Between Categorical Variables–Contingency Tables 3.1 Contingency Tables3.2 Conditional Distributions3.3 Displaying Contingency Tables3.4 Three Categorical Variables 4. Understanding and Comparing Distributions 4.1 Displays for Comparing Groups4.2 Outliers4.3 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 Values5.2 Shifting and Scaling5.3 Normal Models5.4 Working with Normal Percentiles5.5 Normal Probability PlotsReview of Part I: Exploring and Understanding Data II. EXPLORING RELATIONSHIPS BETWEEN VARIABLES 6. Scatterplots, Association, and Correlation 6.1 Scatterplots6.2 Correlation6.3 Warning: Correlation ≠ Causation6.4 Straightening Scatterplots 7. Linear 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 Conditions 8. Regression 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-Expression 9. Multiple 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 VariablesReview of Part II: Exploring Relationships Between Variables III. GATHERING DATA 10. 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 Badly 11. Experiments and Observational Studies 11.1 Observational Studies11.2 Randomized, Comparative Experiments11.3 The Four Principles of Experimental Design11.4 Control Groups11.5 Blocking11.6 ConfoundingReview of Part III: Gathering Data IV. RANDOMNESS AND PROBABILITY 12. From Randomness to Probability 12.1 Random Phenomena12.2 Modeling Probability12.3 Formal Probability 13. Probability Rules! 13.1 The General Addition Rule13.2 Conditional Probability and the General Multiplication Rule13.3 Independence13.4 Picturing Probability: Tables, Venn Diagrams, and Trees13.5 Reversing the Conditioning and Bayes' Rule 14. Random Variables 14.1 Center: The Expected Value14.2 Spread: The Standard Deviation14.3 Shifting and Combining Random Variables14.4 Continuous Random Variables 15. Probability Models 15.1 Bernoulli Trials15.2 The Geometric Model15.3 The Binomial Model15.4 Approximating the Binomial with a Normal Model15.5 The Continuity Correction15.6 The Poisson Model15.7 Other Continuous Random Variables: The Uniform and the ExponentialReview 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 Proportion16.2 When Does the Normal Model Work? Assumptions and Conditions16.3 A Confidence Interval for a Proportion16.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?16.5 Margin of Error: Certainty vs. Precision16.6 Choosing the Sample Size 17. Confidence Intervals for Means 17.1 The Central Limit Theorem17.2 A Confidence Interval for the Mean17.3 Interpreting Confidence Intervals17.4 Picking Our Interval up by Our Bootstraps17.5 Thoughts About Confidence Intervals 18. Testing Hypotheses 18.1 Hypotheses18.2 P-Values18.3 The Reasoning of Hypothesis Testing18.4 A Hypothesis Test for the Mean18.5 Intervals and Tests18.6 P-Values and Decisions: What to Tell About a Hypothesis Test 19. More About Tests and Intervals 19.1 Interpreting P-Values19.2 Alpha Levels and Critical Values19.3 Practical vs. Statistical Significance19.4 ErrorsReview of Part V: Inference for One Parameter VI. INFERENCE FOR RELATIONSHIPS 20. Comparing Groups 20.1 A Confidence Interval for the Difference Between Two Proportions20.2 Assumptions and Conditions for Comparing Proportions20.3 The Two-Sample z-Test: Testing for the Difference Between Proportions20.4 A Confidence Interval for the Difference Between Two Means20.5 The Two-Sample t-Test: Testing for the Difference Between Two Means20.6 Randomization Tests and Confidence Intervals for Two Means20.7 Pooling20.8 The Standard Deviation of a Difference 21. Paired Samples and Blocks 21.1 Paired Data21.2 The Paired t-Test21.3 Confidence Intervals for Matched Pairs21.4 Blocking 22. Comparing Counts 22.1 Goodness-of-Fit Tests22.2 Chi-Square Test of Homogeneity22.3 Examining the Residuals22.4 Chi-Square Test of Independence 23. Inferences for Regression 23.1 The Regression Model23.2 Assumptions and Conditions23.3 Regression Inference and Intuition23.4 The Regression Table23.5 Multiple Regression Inference23.6 Confidence and Prediction Intervals23.7 Logistic Regression23.8 More About RegressionReview of Part VI: Inference for Relationships VII. INFERENCE WHEN VARIABLES ARE RELATED 24. Multiple Regression Wisdom 24.1 Multiple Regression Inference24.2 Comparing Multiple Regression Model24.3 Indicators24.4 Diagnosing Regression Models: Looking at the Cases24.5 Building Multiple Regression Models 25. Analysis of Variance 25.1 Testing Whether the Means of Several Groups Are Equal25.2 The ANOVA Table25.3 Assumptions and Conditions25.4 Comparing Means25.5 ANOVA on Observational Data 26. Multifactor Analysis of Variance 26.1 A Two Factor ANOVA Model26.2 Assumptions and Conditions26.3 Interactions 27. Statistics and Data Science 27.1 Introduction to Data MiningReview of Part VII: Inference When Variables Are Related Parts I - V Cumulative Review Exercises Appendices Answers Credits Indexes Tables and Selected Formulas

Hallmark features of this title Where Are We Going? chapter openers give context for the work students are about to begin within the broader course. Reality Checks ask students to think about whether their answers make sense before interpreting their results. Notation Alerts appear whenever special notation is introduced. The Tech Support section provides instructions for applying the topics covered by the chapter within each of the supported statistics packages. Focused examples are provided as each important concept is introduced, applying the concept usually with real, up-to-the-minute data. Just Checking questions are quick checks throughout the chapter that involve minimal calculation and encourage students to pause and think about what they've just read.

New and updated features of this title Random Matters: This new feature encourages a gradual, cumulative understanding of randomization.Streamlined coverage of descriptive statistics helps students progress more quickly through the first part of the book.For 2 of the most difficult concepts in the introductory course, technology is utilized to improve learning: the idea of a sampling distribution and the reasoning of statistical inference.A third variable is introduced with contingency tables and mosaic plots in Chapter 3 to give students earlier experience with multivariable thinking. Then, following the discussion of correlation and regression as a tool (without inference) in Chapters 6, 7 and 8, multiple regression is introduced in Chapter 9.Expanded and revised Think/Show/Tell Step-by-Step Examples guide students through the process of analyzing a problem through worked examples.New Web tools provide interactive versions of the distribution tables at the back of the book, and tools for randomization inference methods such as the bootstrap and for repeated sampling from larger populations now can be found online.