The R Books

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                             The R Books-2010kaiser

                                  The R Book


Getting Started
Essentials of the R Language
Data Input
Classical Tests
Statistical Modelling
Analysis of Variance
Analysis of Covariance
Generalized Linear Models
Count Data
Count Data in Tables
Proportion Data
Binary Response Variables
Generalized Additive Models
Mixed-Effects Models
Non-linear Regression
Tree Models
Time Series Analysis
Multivariate Statistics
Spatial Statistics
Survival Analysis
Simulation Models
Changing the Look of Graphics
References and Further Reading

Author: Crawley M.J.
Title: The R Book
Publisher: John Wiley & Sons Ltd
Year: 2007
Format: PDF


      Statistics and Data with R. An Applied Approach Through Examples

R, an Open Source software, has become the de facto statistical computing environment. It has an excellent collection of data manipulation and graphics capabilities. It is extensible and comes with a large number of packages that allow statistical analysis at all levels - from simple to advanced - and in numerous fields including Medicine, Genetics, Biology, Environmental Sciences, Geology, Social Sciences and much more. The software is maintained and developed by academicians and professionals and as such, is continuously evolving and up to date. Statistics and Data with R presents an accessible guide to data manipulations, statistical analysis and graphics using R.
Assuming no previous knowledge of statistics or R, the book includes:
A comprehensive introduction to the R language.

Author: Cohen J.Y., Cohen Y.
Title: Statistics and Data with R. An Applied Approach Through Examples
Publisher: Wiley
Year: 2008
Format: PDF


                    Mathematical Statistics with Resampling and R

Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques.
The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods.
Striking a balance between theory, computing, and applications, the authors explore additional topics such as:
Exploratory data analysis
Calculation of sampling distributions
The Central Limit Theorem
Monte Carlo sampling
Maximum likelihood estimation and properties of estimators
Confidence intervals and hypothesis tests
Bayesian methods
Throughout the book, case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the real-world applications of the discussed material. Key definitions and theorems of important probability distributions are collected at the end of the book, and a related website is also available, featuring additional material including data sets, R scripts, and helpful teaching hints.
Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work.

Author: Chihara L.M., Hesterberg T.C.
Title: Mathematical Statistics with Resampling and R
Publisher: Wiley
Year: 2011
Format: PDF

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The R Books/The R Book - 2010kaiser.pdf 27.444 MB
The R Books/Mathematical Statistics with Resampling and R-2010kaiser.pdf 13.693 MB
The R Books/Statistics and Data with R. An Applied Approach Through Examples-2010kaiser.pdf 12.585 MB
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