A course in statistics with R / Prabhanjan Tattar, Suresh Ramaiah, B.G. Manjunath.

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.