1: Introduction to Data

1.1 What Are Data?

1.2 Classifying and Storing Data

1.3 Investigating Data

1.4 Organizing Categorical Data

1.5 Collecting Data to Understand Causality

2: Picturing Variation with Graphs

2.1 Visualizing Variation in Numerical Data

2.2 Summarizing Important Features of a Numerical Distribution

2.3 Visualizing Variation in Categorical Variables

2.4 Summarizing Categorical Distributions

2.5 Interpreting Graphs

3: Numerical Summaries of Center and Variation

3.1 Summaries for Symmetric Distributions

3.2 What's Unusual? The Empirical Rule and z-Scores

3.3 Summaries for Skewed Distributions

3.4 Comparing Measures of Center

3.5 Using Boxplots for Displaying Summaries

4: Regression Analysis: Exploring Associations between Variables

4.1 Visualizing Variability with a Scatterplot

4.2 Measuring Strength of Association with Correlation

4.3 Modeling Linear Trends

4.4 Evaluating the Linear Model

5: Modeling Variation with Probability

5.1 What Is Randomness?

5.2 Finding Theoretical Probabilities

5.3 Associations in Categorical Variables

5.4 Finding Empirical Probabilities

6: Modeling Rando Events: The Normal and Binomial Models

6.1 Probability Distributions Are Models of Random Experiments

6.2 The Normal Model

6.3 The Binomial Model (Optional)

7: Survey Sampling and Inference

7.1 Learning about the World through Surveys

7.2 Measuring the Quality of a Survey

7.3 The Central Limit Theorem for Sample Proportions

7.4 Estimating the Population Proportion with Confidence Intervals

7.5 Comparing Two Population Proportions with Confidence

8: Hypothesis Testing for Population Proportions

8.1 The Essential Ingredients of Hypothesis Testing

8.2 Hypothesis Testing in Four Steps

8.3 Hypothesis Tests in Detail

8.4 Comparing Proportions from Two Populations

9: Inferring Population Means

9.1 Sample Means of Rando Samples

9.2 The Central Limit Theorem for Sample Means

9.4 Hypothesis Testing for Means

9.5 Comparing Two Population Means

9.6 Overview of Analyzing Means

10: Associations between Categorical Variables

10.1 The Basic Ingredients for Testing with Categorical Variables

10.2 The Chi-Square Test for Goodness of Fit

10.3 Chi-Square Tests for Associations between Categorical Variables

10.4 Hypothesis Tests When Sample Sizes Are Small

11: Multiple Comparisons and Analysis of Variance

11.1 Multiple Comparisons

11.2 The Analysis of Variance

11.3 The ANOVA Test

11.4 Post-Hoc Procedures

12: Experimental Design: Controlling Variation

12.1 Variation Out of Control

12.2 Controlling Variation in Surveys

13: Inference without Normality

13.1 Transforming Data

13.2 The Sign Test for Paired Data

13.3 Mann-Whitney Test for Two Independent Groups

13.4 Randomization Tests

14: Inference for Regression

14.1 The Linear Regression Model

14.2 Using the Linear Model

14.3 Predicting Values and Estimating Means