**Introduction **

Before You Start

Initial Data Analysis

When to Use Linear Modeling

History

**Estimation **

Linear Model

Matrix Representation

Estimating b

Least Squares Estimation

Examples of Calculating ˆb

Example

QR Decomposition

Gauss–Markov Theorem

Goodness of Fit

Identifiability

Orthogonality

**Inference **

Hypothesis Tests to Compare Models

Testing Examples

Permutation Tests

Sampling

Confidence Intervals for b

Bootstrap Confidence Intervals

**Prediction **

Confidence Intervals for Predictions

Predicting Body Fat

Autoregression

What Can Go Wrong with Predictions?

**Explanation **

Simple Meaning

Causality

Designed Experiments

Observational Data

Matching

Covariate Adjustment

Qualitative Support for Causation

**Diagnostics **

Checking Error Assumptions

Finding Unusual Observations

Checking the Structure of the Model

Discussion

**Problems with the Predictors **

Errors in the Predictors

Changes of Scale

Collinearity

**Problems with the Error **

Generalized Least Squares

Weighted Least Squares

Testing for Lack of Fit

Robust Regression

**Transformation **

Transforming the Response

Transforming the Predictors

Broken Stick Regression

Polynomials

Splines

Additive Models

More Complex Models

**Model Selection **

Hierarchical Models

Testing-Based Procedures

Criterion-Based Procedures

Summary

**Shrinkage Methods **

Principal Components

Partial Least Squares

Ridge Regression

Lasso

**Insurance Redlining—A Complete Example **

Ecological Correlation

Initial Data Analysis

Full Model and Diagnostics

Sensitivity Analysis

Discussion

**Missing Data **

Types of Missing Data

Deletion

Single Imputation

Multiple Imputation

**Categorical Predictors **

A Two-Level Factor

Factors and Quantitative Predictors

Interpretation with Interaction Terms

Factors with More than Two Levels

Alternative Codings of Qualitative Predictors

**One Factor Models **

The Model

An Example

Diagnostics

Pairwise Comparisons

False Discovery Rate

**Models with Several Factors **

Two Factors with No Replication

Two Factors with Replication

Two Factors with an Interaction

Larger Factorial Experiments

**Experiments with Blocks **

Randomized Block Design

Latin Squares

Balanced Incomplete Block Design

**Appendix: About R **

**Bibliography**

**Index**