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A01=Arnaud Guyader
A01=Eric Matzner-Lober
A01=Francois Husson
A01=Julie Josse
A01=Laurent Rouviere
A01=Maela Kloareg
A01=Nicolas Jegou
A01=Pierre-Andre Cornillon
Active Data Set
advanced statistical modeling in R
Agrocampus Ouest
AHC
Alternative Hypothesis H1
An Overview Of R
ANOVA Table
Author_Arnaud Guyader
Author_Eric Matzner-Lober
Author_Francois Husson
Author_Julie Josse
Author_Laurent Rouviere
Author_Maela Kloareg
Author_Nicolas Jegou
Author_Pierre-Andre Cornillon
Category=PBT
Classification Error Rate
cluster analysis approaches
data visualization techniques
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Explanatory Qualitative Variable
FactoMineR Package
FALSE FALSE
FALSE FALSE FALSE
Hypothesis Test
hypothesis testing procedures
Mac OS
Making Programs With R
Mass Package
Min 1Q Median 3Q Max
Multiple Correspondence Analysis
multivariate data analysis
NA NA NA
NA NA NA NA
Ozone Dataset
PLS
PLS Component
PLS Regression
Preparing Data
Qualitative Variable
regression analysis methods
Residual Standard Error
Shapiro Wilk Test
statistical computing
Statistical Methods
TRUE FALSE FALSE FALSE FALSE
Vec1 Vec2

Product details

  • ISBN 9781138469341
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 28 Sep 2017
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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.

Datasets and all the results described in this book are available on the book‘s webpage at http://www.agrocampus-ouest.fr/math/RforStat

Pierre-Andre Cornillon, Arnaud Guyader, Francois Husson, Nicolas Jegou, Julie Josse, Maela Kloareg, ric Matzner-Lober, Laurent Rouvière

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