Because real data are often messy and inference requires clean data, data analysis is an essential preliminary to inference. That is why data analysis is presented first, as Part 1.
Part I Data
Chapter 1 Examining Distributions
1.1 Displaying Distributions with Graphs
1.2 Describing Distributions with Numbers
1.3 The Normal Distributions
The first of two Chapters on data analysis: shows students how to look at data and summarize them graphically and numerically, introducing sampling distributions, repeated samplings, and standard deviation.
Chapter 2 Examining Relationships
2.1 Scatterplots
2.2 Correlation
2.3 Least-Squares Regression
2.4 Cautions about Correlation and Regression
2.5 Relations in Categorical Data
The second Chapter on data analysis: shows students how to analyze and summarize, graphically and numerically, data with two variables, introducing relationships between variables, correlation, and providing a preliminary introduction to least-squares regression.
Chapter 3 Producing Data
3.1 Designing Samples
3.2 Designing Experiments
3.3 Toward Statistical Inference
3.4 NEW Commentary: Data Ethics
Teaches students to look more deeply at where data sets come from and how to recognize good data from bad data.
Part II Probability and Inference
Chapter 4 Probability and Sampling Distributions
4.1 Randomness
4.2 Probability Models
4.3 Random Variables
4.4 The Sampling Distribution of a Sample Mean
The probability material that is needed to understand statistical inference.
Chapter 5 Probability Theory
5.1 General Probability Rules
5.2 The Binomial Distributions
5.3 The Poisson Distributions
5.4 Conditional Probability
Additional probability material in a more traditional manner; optional.
Chapter 6 Introduction to Inference
6.1 Estimating with Confidence
6.2 Tests of Significance
6.3 Using Significance Tests
6.4 Power and Inference as a Decision
From Chapter 6 on, the book presents statistical inference, still encouraging students to analyze the data rather than quickly choosing a test from Excel.
Chapter 7 Inference for Distributions
7.1 Inference for the Mean of a Population
7.2 Comparing Two Means
7.3 Optional Topics in Comparing Distributions
Chapter 8 Inference for Proportions
8.1 Inference for a Single Proportion
8.2 Comparing Two Proportions
Part III Topics in Inference
Chapter 9 Inference for Two-Way Tables
9.1 Analysis of Two-Way Tables
9.2 Formulas and Models for Two-Way Tables
Chapter 10 Inference for Regression
10.1 Inference about the Regression Model
10.2 Using the Regression Line
10.3 Some Details of Regression Inference
Chapter 11 Multiple Regression
11.1 Data Analysis for Multiple Regression
11.2 Inference for Multiple Regression
11.3 Multiple Regression Model Building
Chapter 12 Statistics for Quality: Control and Capability
12.1 Statistical Process Control
12.2 Using Control Charts
12.3 Process Capability Indexes
12.4 Control Charts for Sample Proportions
Chapter 13 Time Series Forecasting
13.1 Trends and Seasons
13.2 Time Series Models
Chapter 14 One-Way Analysis of Variance
14.1 One-Way Analysis of Variance
14.2 Comparing Group Means
14.3 The Power of the ANOVA Test
Part IV Optional Individual Companion Chapters
Chapter 15 Two-Way Analysis of Variance
15.1 The Two-Way ANOVA Model
15.2 Inference for Two-Way ANOVA
Chapter 16 Nonparametric Tests
16.1 The Wilcoxon Rank Sum Test
16.2 The Wilcoxon Signed Rank Test
16.3 The Kruskal-Wallis Test
Chapter 17 Logistic Regression
17.1 The Logistic Regression Model
17.2 Inference for Logistic Regression
17.3 Multiple Logistic Regression
Chapter 18 Bootstrap Methods and Permutation Tests
18.1 Why Resampling?
18.2 Introduction to Bootstrapping
18.3 Bootstrap Distributions and Standard Errors
18.4 How Accurate is a Bootstrap Distribution?
18.5 Bootstrap Confidence Intervals
18.6 Significance Testing Using Permutation Tests
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