000			02137nam a2200253 a 4500
020			_a1439881456 (paperback)
020			_a9781439881453 (paperback)
082			_a310 Cornillon 27670 1/e 2012 Statistics
100	1		_aCornillon, Pierre-Andre _91528
245	1	0	_a[R] for Statistics
250			_a1st ed.
260			_aUSA: _bChapman and Hall/CRC; _c2012.
300			_a320 p. ;
520			_aAlthough 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/RforSta
650			_aStatistics, _9109
700			_aGuyader, Arnau _91529
700			_aHusson, Francoi _91530
700			_aJegou, Nicola _91531
700			_aJosse, Juli _91532
700			_aKloareg, Mael _91533
700			_aMatzner-Lober, Eri _91534
700			_aRouvière, Laure _91535
942			_cBK
999			_c715 _d715