Includes bibliographical references and index.

The Preliminaries. Why R?
The R Basics
Data Preparation and Other Tricks
Exploratory Data Analysis
Probability and Inference. Probability Theory
Probability and Sampling Distributions
Parametric Inference
Nonparametric Inference
Bayesian Inference
Stochastic Processes and Monte Carlo. Stochastic Processes
Monte Carlo Computations
Linear Models. Linear Regression Models
Experimental Designs
Multivariate Statistical Analysis
I
Multivariate Statistical Analysis
II
Categorical Data Analysis
Generalized Linear Models
Appendix A: Open Source Software: An Epilogue
Appendix B: The Statistical Tables

Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models