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 E