R for Statistics

Title: R for Statistics
Author: Arnaud Guyader, Eric Matzner-Lober, Francois Husson, Julie Josse, Laurent Rouviere, Maela Kloareg, Nicolas Jegou, Pierre-Andre Cornillon
ISBN: 1439881456 / 9781439881453
Format: Soft Cover
Pages: 320
Publisher: CHAPMAN & HALL
Year: 2012
Availability: Out of Stock
Special Indian Edition.

Tab Article

Although there are currently a wide variety of software packages suitable for the modern statistician, R has the triple advantage of being comprehensive, widespread, and free. Published in 2008, the second edition of Statistiques avec R enjoyed great success as an R guidebook in the French-speaking world. Translated and updated, R for Statistics includes a number of expanded and additional worked examples.

Organized into two sections, the book focuses first on the R software, then on the implementation of traditional statistical methods with R.

Focusing on the R software, the first section covers:

  • Basic elements of the R software and data processing
  • Clear, concise visualization of results, using simple and complex graphs
  • Programming basics: pre-defined and user-created functions

The second section of the book presents R methods for a wide range of traditional statistical data processing techniques, including:

  • Regression methods
  • Analyses of variance and covariance
  • Classification methods
  • Exploratory multivariate analysis
  • Clustering methods
  • Hypothesis tests

After a short presentation of the method, the book explicitly details the R command lines and gives commented results. Accessible to novices and experts alike, R for Statistics is a clear and enjoyable resource for any scientist.

Tab Article

Preface

Part I : An Overview of R
Chapter 1
: Main Concepts
Chapter 2 : Preparing Data
Chapter 3 : R Graphics
Chapter 4 : Making Programs with R

Part II : Statistical Methods
Chapter 5 :
Introduction to the Statistical Methods
Chapter 6 : A Quick Start with R
Chapter 7 : Hypothesis Test
Chapter 8 : Regression
Chapter 9 : Analysis of Variance and Covariance
Chapter 10 : Classification
Chapter 11 : Exploratory Multivariate Analysis
Chapter 12 : Clustering

Appendix A : The Most Useful Functions
Appendix B : Writing a Formula for the Models
Appendix C : The Rcmdr Package
Appendix D : The FactoMineR Package
Appendix E : Answers to the Exercises
Index