#### Key features:

- Integrates R basics with statistical concepts
- Provides graphical presentations inclusive of mathematical expressions
- Aids understanding of limit theorems of probability with and without the simulation approach
- Presents detailed algorithmic development of statistical models from scratch
- Includes practical applications with over 50 data sets

#### Table of Contents

Part I: The Preliminaries

Chapter 1: Why R?

Chapter 2: The R Basics

Chapter 3: Data Preparation and Other Tricks

Chapter 4: Exploratory Data Analysis

Part II: Probability and Inference

Chapter 5: Probability Theory

Chapter 6: Probability and Sampling Distributions

Chapter 7: Parametric Inference

Chapter 8: Nonparametric Inference

Chapter 9: Bayesian Inference

Part III: Stochastic Processes and Monte Carlo

Chapter 10: Stochastic Processes

Chapter 11: Monte Carlo Computations

Part IV: Linear Models

Chapter 12: Linear Regression Models

Chapter 13: Experimental Designs

Chapter 14: Multivariate Statistical Analysis - I

Chapter 15: Multivariate Statistical Analysis - II

Chapter 16: Categorical Data Analysis

Chapter 17: Generalized Linear Models

Appendix A: Open Source Software–An Epilogue

Appendix B: The Statistical Tables