John Fox & Sanford Weisberg 
An R Companion to Applied Regression [EPUB ebook] 

1. Getting Started with R and RStudio
Projects in RStudio
R Basics
Fixing Errors and Getting Help
Organizing Your Work in R and RStudio
An Extended Illustration
R Functions for Basic Statistics
Generic Functions and Their Methods*
2. Reading and Manipulating Data
Data Input
Managing Data
Working With Data Frames
Matrices, Arrays, and Lists
Dates and Times
Character Data
Large Data Sets in R*
Complementary Reading and References
3. Exploring and Transforming Data
Examining Distributions
Examining Relationships
Examining Multivariate Data
Transforming Data
Point Labeling and Identication
Scatterplot Smoothing
Complementary Reading and References
4. Fitting Linear Models
The Linear Model
Linear Least-Squares Regression
Predictor Effect Plots
Polynomial Regression and Regression Splines
Factors in Linear Models
Linear Models with Interactions
More on Factors
Too Many Regressors*
The Arguments of the lm Function
Complementary Reading and References
5. Standard Errors, Confidence Intervals, Tests
Coefficient Standard Errors
Confidence Intervals
Testing Hypotheses About Regression Coefficients
Complementary Reading and References
6. Fitting Generalized Linear Models
The Structure of GLMs
The glm() Function in R
GLMs for Binary-Response Data
Binomial Data
Poisson GLMs for Count Data
Loglinear Models for Contingency Tables
Multinomial Response Data
Nested Dichotomies
The Proportional-Odds Model
Extensions
Arguments to glm()
Fitting GLMs by Iterated Weighted Least-Squares*
Complementary Reading and References
7. Fitting Mixed-Effects Models
Background: The Linear Model Revisited
Linear Mixed-Effects Models
Generalized Linear Mixed Models
Complementary Reading
8. Regression Diagnostics
Residuals
Basic Diagnostic Plots
Unusual Data
Transformations After Fitting a Regression Model
Non-Constant Error Variance
Diagnostics for Generalized Linear Models
Diagnostics for Mixed-Effects Models
Collinearity and Variance-Inflation Factors
Additional Regression Diagnostics
Complementary Reading and References
9. Drawing Graphs
A General Approach to R Graphics
Putting It Together: Local Linear Regression
Other R Graphics Packages
Complementary Reading and References
10. An Introduction to R Programming
Why Learn to Program in R?
Defining Functions: Preliminary Examples
Working With Matrices*
Conditionals, Loops, and Recursion
Avoiding Loops
Optimization Problems*
Monte-Carlo Simulations*
Debugging R Code*
Object-Oriented Programming in R*
Writing Statistical-Modeling Functions in R*
Organizing Code for R Functions
Complementary Reading and References

Sanford Weisberg is Professor Emeritus of statistics at the University of Minnesota.  He has also served as the director of the University′s Statistical Consulting Service, and has worked with hundreds of social scientists and others on the statistical aspects of their research.  He earned a BA in statistics from the University of California, Berkeley, and a Ph.D., also in statistics, from Harvard University, under the direction of Frederick Mosteller.  The author of more than 60 articles in a variety of areas, his methodology research has primarily been in regression analysis, including graphical methods, diagnostics, and computing.  He is a fellow of the American Statistical Association and former Chair of its Statistical Computing Section.  He is the author or coauthor of several books and monographs, including the widely used textbook Applied Linear Regression, which has been in print for almost forty years.