000			02253nam a2200265Ia 4500
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005			20251118171459.0
008			220909b |||||||| |||| 00| 0 eng d
020			_a9781439881453
037			_cTextbook
040			_aCSL _beng _cCSL
041			_aeng
084			_aB28:(D65,8(B)) Q2 TB _qCSL
100			_aCornillon, Pierre-Andre _eauthor _9852142
245		0	_aR for Statistics
260			_aLondon : _bCRC , _c2012 .
300			_axiv,306p.
500			_aIncluded Bibliography 295-296p.; Index of the functions 297-300p.; Index 301-306p.
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
650			_aStatistical methods. _9852143
650			_aR graphic. _9852144
700			_aCornillon, Pierre-Andre _eauthor. _9852142
942			_hB28:(D65,8(B)), Q2 TB _cTB _2CC _n0
999			_c8802 _d8802