Computational Statistics: An Introduction to R

Günther Sawitzki

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January 26, 2009 by Chapman and Hall/CRC
Textbook - 264 Pages - 12 Color
ISBN 9781420086782 - CAT# C6782

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Features

  • Focuses on the underlying concepts of statistics
  • Covers distribution diagnostics, Monte Carlo tests, analysis of variance, general linear models, distribution-free tests, and dimension reduction
  • Includes numerous exercises and real-world examples from biology, medicine, and more
  • Provides an appendix that describes elements of R by topic
  • Offers the full R source code for all examples, selected solutions, and other ancillary material on <http://sintro.r-forge.r-project.org/>

Summary

Suitable for a compact course or self-study, Computational Statistics: An Introduction to R illustrates how to use the freely available R software package for data analysis, statistical programming, and graphics. Integrating R code and examples throughout, the text only requires basic knowledge of statistics and computing.

This introduction covers one-sample analysis and distribution diagnostics, regression, two-sample problems and comparison of distributions, and multivariate analysis. It uses a range of examples to demonstrate how R can be employed to tackle statistical problems. In addition, the handy appendix includes a collection of R language elements and functions, serving as a quick reference and starting point to access the rich information that comes bundled with R.

Accessible to a broad audience, this book explores key topics in data analysis, regression, statistical distributions, and multivariate statistics. Full of examples and with a color insert, it helps readers become familiar with R.

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