Titre : |
A handbook of statistical analyses using R |
Type de document : |
livre |
Auteurs : |
Everitt, B.S. ; Hothorn, T. |
Editeur : |
Boca Raton : Chapman & Hall/CRC |
AnnÃ©e de publication : |
2006 |
Importance : |
275 p. |
ISBN/ISSN/EAN : |
978-1-58488-539-9 |
Langues : |
Anglais (eng) |
Mots-clÃ©s : |
Handbook R (Computer program language) Statistical analysis |
RÃ©sumÃ© : |
R is dynamic, to say the least. More precisely, it is organic, with new functionality and add-on packages appearing constantly. And because of its open-source nature and free availability, R is quickly becoming the software of choice for statistical analysis in a variety of fields. Doing for R what Everitt's other Handbooks have done for S-PLUS, STATA, SPSS, and SAS, A Handbook of Statistical Analyses Using R presents straightforward, self-contained descriptions of how to perform a variety of statistical analyses in the R environment. From simple inference to recursive partitioning and cluster analysis, eminent experts Everitt and Hothorn lead you methodically through the steps, commands, and interpretation of the results, addressing theory and statistical background only when useful or necessary. They begin with an introduction to R, discussing the syntax, general operators, and basic data manipulation while summarizing the most important features. Numerous figures highlight R's strong graphical capabilities and exercises at the end of each chapter reinforce the techniques and concepts presented. All data sets and code used in the book are available as a downloadable package from CRAN, the R online archive. * Explains systematically how to use R to perform a wide variety of statistical analyses * Emphasizes practical application and interpretation of results rather than focusing on the theory behind the analyses * Offers an introduction to R, including a summary of the most important features * Covers simple inference, generalized linear models, multilevel models, longitudinal data, classification and regression trees, discriminant analysis, and much more * Includes abundant figures and exercises to demonstrate the capabilities of R and reinforce the methods presented |
En ligne : |
http://books.google.fr/books?id=w--RcQQfnRUC&dq=a+handbook+of+statistical+analys [...] |
A handbook of statistical analyses using R [livre] / Everitt, B.S. ; Hothorn, T. . - Boca Raton : Chapman & Hall/CRC, 2006 . - 275 p. ISBN : 978-1-58488-539-9 Langues : Anglais ( eng)
Mots-clÃ©s : |
Handbook R (Computer program language) Statistical analysis |
RÃ©sumÃ© : |
R is dynamic, to say the least. More precisely, it is organic, with new functionality and add-on packages appearing constantly. And because of its open-source nature and free availability, R is quickly becoming the software of choice for statistical analysis in a variety of fields. Doing for R what Everitt's other Handbooks have done for S-PLUS, STATA, SPSS, and SAS, A Handbook of Statistical Analyses Using R presents straightforward, self-contained descriptions of how to perform a variety of statistical analyses in the R environment. From simple inference to recursive partitioning and cluster analysis, eminent experts Everitt and Hothorn lead you methodically through the steps, commands, and interpretation of the results, addressing theory and statistical background only when useful or necessary. They begin with an introduction to R, discussing the syntax, general operators, and basic data manipulation while summarizing the most important features. Numerous figures highlight R's strong graphical capabilities and exercises at the end of each chapter reinforce the techniques and concepts presented. All data sets and code used in the book are available as a downloadable package from CRAN, the R online archive. * Explains systematically how to use R to perform a wide variety of statistical analyses * Emphasizes practical application and interpretation of results rather than focusing on the theory behind the analyses * Offers an introduction to R, including a summary of the most important features * Covers simple inference, generalized linear models, multilevel models, longitudinal data, classification and regression trees, discriminant analysis, and much more * Includes abundant figures and exercises to demonstrate the capabilities of R and reinforce the methods presented |
En ligne : |
http://books.google.fr/books?id=w--RcQQfnRUC&dq=a+handbook+of+statistical+analys [...] |
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