* Indicates optional section

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 Studies
    • 11.1 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: FROM THE DATA AT HAND TO THE WORLD AT LARGE

12. From Randomness to Probability
    • 12.1 Random Phenomena
    • 12.2 Modeling Probability
    • 12.3 Formal Probability
    • 12.4 Conditional Probability and the General Multiplication Rule
    • 12.5 Independence
    • 12.6 Picturing Probability: Tables, Venn Diagrams, and Trees
    • 12.7 Reversing the Conditioning and Bayes' Rule
13. Sampling Distributions and Confidence Intervals for Proportions
    • 13.1 The Sampling Distribution for a Proportion
    • 13.2 When Does the Normal Model Work? Assumptions and Conditions
    • 13.3 A Confidence Interval for a Proportion
    • 13.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?
    • 13.5 Margin of Error: Certainty vs. Precision
    • 13.6 * Choosing the Sample Size
14. Confidence Intervals for Means
    • 14.1 The Central Limit Theorem
    • 14.2 A Confidence interval for the Mean
    • 14.3 Interpreting confidence intervals
    • 14.4 * Picking our Interval Up by our Bootstraps
    • 14.5 Thoughts about Confidence Intervals
15. Testing Hypotheses
    • 15.1 Hypotheses
    • 15.2 P-values
    • 15.3 The Reasoning of Hypothesis Testing
    • 15.4 A Hypothesis Test for the Mean
    • 15.5 Intervals and Tests
    • 15.6 P-Values and Decisions: What to Tell About a Hypothesis Test
16. More About Tests and Intervals
    • 16.1 Interpreting P-values
    • 16.2 Alpha Levels and Critical Values
    • 16.3 Practical vs. Statistical Significance
    • 16.4 Errors
    • Review of Part IV: From the Data at Hand to the World at Large

V: INFERENCE FOR RELATIONSHIPS

17. Comparing Groups
    • 17.1 A Confidence Interval for the Difference Between Two Proportions
    • 17.2 Assumptions and Conditions for Comparing Proportions
    • 17.3 The Two-Sample z-Test: Testing the Difference Between Proportions
    • 17.4 A Confidence Interval for the Difference Between Two Means
    • 17.5 The Two-Sample t-Test: Testing for the Difference Between Two Means
    • 17.6 * Randomization-Based Tests and Confidence Intervals for Two Means
    • 17.7 * Pooling
    • 17.8 * The Standard Deviation of a Difference
18. Paired Samples and Blocks
    • 18.1 Paired Data
    • 18.2 The Paired t-Test
    • 18.3 Confidence Intervals for Matched Pairs
    • 18.4 Blocking
19. Comparing Counts
    • 19.1 Goodness-of-Fit Tests
    • 19.2 Chi-Square Tests of Homogeneity
    • 19.3 Examining the Residuals
    • 19.4 Chi-Square Test of Independence
20. Inferences for Regression
    • 20.1 The Regression Model
    • 20.2 Assumptions and Conditions
    • 20.3 Regression Inference and Intuition
    • 20.4 The Regression Table
    • 20.5 Multiple Regression Inference
    • 20.6 Confidence and Prediction Intervals
    • 20.7 * Logistic Regression
    • 20.8 * More About Regression
    • Review of Part V: Inference for Relationships

Parts I–V Cumulative Review Exercises

Appendixes:

1. Answers
2. Credits
3. Indexes
4. Tables and Selected Formulas