R for Statistics Pierre-André Cornillon

ISBN: 9781439881453

Published: in 2008, the second edition of Statistiques avec R enjoyed greatMoreAlthough 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 Less

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Other Books

Paperback

320 pages


Description

R for Statistics  by  Pierre-André Cornillon

R for Statistics by Pierre-André Cornillon
in 2008, the second edition of Statistiques avec R enjoyed greatMoreAlthough 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 Less

Friends’ Reviews

To see what your friends thought of this book,please sign up.

Community Reviews

Other Books | Paperback | PDF, EPUB, FB2, DjVu, talking book, mp3, ZIP | 320 pages | ISBN: 9781439881453 | 6.60 Mb


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 greatMoreAlthough 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 functionsThe 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 testsAfter 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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