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Preface Part I: Exploring and Understanding Data 1. Stats Starts Here 1.1 What Is Statistics? 1.2 Data 1.3 Variables 2. Displaying and Describing Categorical Data 2.1 Summarizing and Displaying a Single Categorical variable 2.2 Exploring the Relationship Between Two Categorical variables 3. Displaying and Summarizing Quantitative Data 3.1 Displaying quantitative variables 3.2 Shape 3.3 Center 3.4 Spread 3.5 Boxplots and 5-Number Summaries 3.6 The Center of Symmetric Distributions: The Mean 3.7 The Spread of Symmetric Distributions: The Standard Deviation 3.8 Summary—What to Tell About a quantitative variable 4. Understanding and Comparing Distributions 4.1 Comparing Groups with Histograms 4.2 Comparing Groups with Boxplots 4.3 Outliers 4.4 Timeplots: Order, Please! 4.5 Re-Expressing Data: A First Look 5. The Standard Deviation as a Ruler and the Normal Model 5.1 Standardizing with z-Scores 5.2 Shifting and Scaling 5.3 Normal Models 5.4 Finding Normal Percentiles 5.5 Normal Probability Plots Part 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 9. Re-expressing Data: Get It Straight! 9.1 Straightening Scatterplots – The Four Goals 9.2 Finding a Good Re-Expression Part III: Gathering Data 10. Understanding Randomness 10.1 What Is Randomness? 10.2 Simulating by Hand 11. Sample Surveys 11.1 The Three Big Ideas of Sampling 11.2 Populations and Parameters 11.3 Simple Random Samples 11.4 Other Sampling Designs 11.5 From the Population to the Sample: You Can’t Always Get What You Want 11.6 The valid Survey 11.7 Common Sampling Mistakes, or How to Sample Badly 12. Experiments and Observational Studies 12.1 Observational Studies 12.2 Randomized, Comparative Experiments 12.3 The Four Principles of Experimental Design 12.4 Control Treatments 12.5 Blocking 12.6 Confounding Part IV: Randomness and Probability 13. From Randomness to Probability 13.1 Random Phenomena 13.2 Modeling Probability 13.3 Formal Probability 14. Probability Rules! 14.1 The General Addition Rule 14.2 Conditional Probability and the General Multiplication Rule 14.3 Independence 14.4 Picturing Probability: Tables, Venn Diagrams, and Trees 14.5 Reversing the Conditioning and Bayes’ Rule 15. Random Variables 15.1 Center: The Expected value 15.2 Spread: The Standard Deviation 15.3 Shifting and Combining Random variables 15.4 Continuous Random variables 16. Probability Models 16.1 Bernoulli Trials 16.2 The Geometric Model 16.3 The Binomial Model 16.4 Approximating the Binomial with a Normal Model 16.5 The Continuity Correction 16.6 The Poisson Model 16.7 Other Continuous Random Variables: The Uniform and the Exponential Part V: From the Data at Hand to the World at Large 17. Sampling Distribution Models 17.1 Sampling Distribution of a Proportion 17.2 When Does the Normal Model Work? Assumptions and Conditions 17.3 The Sampling Distribution of Other Statistics 17.4 The Central Limit Theorem: The Fundamental Theorem of Statistics 17.5 Sampling Distributions: A Summary 18. Confidence Intervals for Proportions 18.1 A Confidence Interval 18.2 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean? 18.3 Margin of Error: Certainty vs. Precision 18.4 Assumptions and Conditions 19. Testing Hypotheses About Proportions 19.1 Hypotheses 19.2 P-values 19.3 The Reasoning of Hypothesis Testing 19.4 Alternative Alternatives 19.5 P-values and Decisions: What to Tell About a Hypothesis Test 20. Inferences About Means 20.1 Getting Started: The Central Limit Theorem (Again) 20.2 Gosset’s t 20.3 Interpreting Confidence Intervals 20.4 A Hypothesis Test for the Mean 20.5 Choosing the Sample Size 21. More About Tests and Intervals 21.1 Choosing Hypotheses 21.2 How to Think About P-values 21.3 Alpha Levels 21.4 Critical values for Hypothesis Tests 21.5 Errors Part VI: Accessing Associations Between Variables 22. Comparing Groups 22.1 The Standard Deviation of a Difference 22.2 Assumptions and Conditions for Comparing Proportions 22.3 A Confidence Interval for the Difference Between Two Proportions 22.4 The Two Sample z-Test: Testing for the Difference Between Proportions 22.5 A Confidence Interval for the Difference Between Two Means 22.6 The Two-Sample t-Test: Testing for the Difference Between Two Means 22.7 The Pooled t-Test: Everyone into the Pool? 23. Paired Samples and Blocks 23.1 Paired Data 23.2 Assumptions and Conditions 23.3 Confidence Intervals for Matched Pairs 23.4 Blocking 24. Comparing Counts 24.1 Goodness-of-Fit Tests 24.2 Chi-Square Test of Homogeneity 24.3 Examining the Residuals 24.4 Chi-Square Test of Independence 25. Inferences for Regression 25.1 The Population and the Sample 25.2 Assumptions and Conditions 25.3 Intuition About Regression Inference 25.4 Regression Inference 25.5 Standard Errors for Predicted values 25.6 Confidence Intervals for Predicted values 25.7 Logistic Regression Part VII: Inference When Variables Are Related 26. Analysis of Variance 26.1 Testing Whether the Means of Several Groups Are Equal 26.2 The ANOVA Table 26.3 Assumptions and Conditions 26.4 Comparing Means 26.5 ANOVA on Observational Data 27. Multifactor Analysis of Variance 27.1 A Two Factor ANOVA Model 27.2 Assumptions and Conditions 27.3 Interactions 28. Multiple Regression 28.1 What Is Multiple Regression? 28.2 Interpreting Multiple Regression Coefficients 28.3 The Multiple Regression Model—Assumptions and Conditions 28.4 Multiple Regression Inference 28.5 Comparing Multiple Regression Models 29. Multiple Regression Wisdom 29.1 Indicators 29.2 Diagnosing Regression Models: Looking at the Cases 29.3 Building Multiple Regression Models 29.4 Building Multiple Regression Models Sequentially Appendixes A: Answers B: Photo Acknowledgments C: Index D: Tables and Selected Formulas Table of Contents
Get Stats: Data and Models Plus NEW MyLab Statistics with Pearson eText Package, 4th Edition by Richard D. De Veaux, Williams College Paul F. Velleman, Cornell University David E. Bock, Ithaca High School (Retired)
