The book is online and available here.

This document is keyed to the table of contents of the book.

1.1 Case study: using stents to prevent strokes

1.2 Data basics

- Activity: Response and Explanatory Variables

1.3 Sampling principles and strategies

1.4 Experiments

- Activity: Sampling Bias and the Confidence Interval
- Acitivy: Intervention and Prediction

- 2.1 Examining numerical data
- Activity: Data and Point Plots
- Activity: Shapes of distributions

- 2.2 Considering categorical data
- Activity: Data and Point Plots

- 2.3 Case study: malaria vaccine

- 3.1 Defining probability
- 3.2 Conditional probability
- 3.3 Sampling from a small population
- 3.4 Random variables
- 3.5 Continuous distributions

- 4.1 Normal distribution
- Activity: Parameters and the Normal Distribution
- ActivityCommon, Uncommon, and Rare

- 4.2 Geometric distribution
- 4.3 Binomial distribution
- 4.4 Negative binomial distribution
- 4.5 Poisson distribution

- 5.1 Point estimates and sampling variability
- Activity: What is a Confidence Interval
- Activity: Sampling Bias and the Confidence Interval

- 5.2 Confidence intervals for a proportion
- 5.3 Hypothesis testing for a proportion

- 6.1 Inference for a single proportion
- 6.2 Difference of two proportions
- 6.3 Testing for goodness of fit using chi-square
- 6.4 Testing for independence in two-way tables

- 7.1 One-sample means with the t-distribution
- 7.2 Paired data
- Activity: Comparing Two Confidence Intervals
- Activity: Comparing Two Groups

- 7.4 Power calculations for a difference of means
- 7.5 Comparing many means with ANOVA

- 8.1 Fitting a line, residuals, and correlation
- Activity: Introducing Linear Regression
- Activity: Describing Relationship Patterns in Words and Numbers
- Activity: How much is explained?

- 8.2 Least squares regression
- 8.3 Types of outliers in linear regression
- 8.4 Inference for linear regression

- 9.1 Introduction to multiple regression
- Activity: Introducing Linear Regression
- Activity: Describing Relationship Patterns in Words and Numbers

- 9.2 Model selection
- 9.3 Checking model conditions using graphs
- 9.4 Multiple regression case study: Mario Kart
- 9.5 Introduction to logistic regression