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