A Handbook of Statistical Analyses Using R

A Handbook of Statistical Analyses Using R

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Description

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. "A Handbook of Statistical Analyses Using R" is the perfect guide for newcomers as well as seasoned users of R who want concrete, step-by-step guidance on how to use the software easily and effectively for nearly any statistical analysis.show more

Product details

  • Paperback | 304 pages
  • 149.86 x 228.6 x 17.78mm | 249.47g
  • Taylor & Francis Inc
  • CRC Press Inc
  • Bosa Roca, United States
  • English
  • 100 black & white illustrations, 56 black & white tables
  • 1584885394
  • 9781584885399
  • 912,790

Table of contents

An Introduction to R. Simple Inference. Conditional Inference. Analysis of Variance. Multiple Linear Regression. Logistic Regression and Generalised Linear Models. Density Estimation. Recursive Partitioning. Survival Analysis. Analysing Longitudinal Data I. Analysing Longitudinal Data II. Meta-Analysis. Principal Component Analysis. Multidimensional Scaling. Cluster Analysis. Bibliography. Index.show more

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32 ratings
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1 3% (1)
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