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Review A Course in Statistics with R by Tattar P.N. et. al

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A Course in Statistics with R A Course in Statistics with R

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.

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

Read 1634 times Last modified on Monday, 19 September 2016 17:36

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