# A Course in Statistics with R

PDF, ePUB

- eBook:A Course in Statistics with R
- Author:Prabhanjan N. Tattar, Suresh Ramaiah, B. G. Manjunath
- Edition:1 edition
- Categories:
- Data:May 2, 2016
- ISBN:1119152720
- ISBN-13:9781119152729
- Language:English
- Pages:692 pages
- Format:PDF, ePUB

**Book Description**

*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

**Content**

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

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