Stats: Data and Models, Global Edition

Table of Contents:
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 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 Studies11.1  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