9781420079333-1420079336-A Handbook of Statistical Analyses Using R, Second Edition

A Handbook of Statistical Analyses Using R, Second Edition

ISBN-13: 9781420079333
ISBN-10: 1420079336
Edition: 2
Author: Torsten Hothorn, Brian S. Everitt
Publication date: 2009
Publisher: Chapman and Hall/CRC
Format: Paperback 376 pages
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Book details

ISBN-13: 9781420079333
ISBN-10: 1420079336
Edition: 2
Author: Torsten Hothorn, Brian S. Everitt
Publication date: 2009
Publisher: Chapman and Hall/CRC
Format: Paperback 376 pages

Summary

A Handbook of Statistical Analyses Using R, Second Edition (ISBN-13: 9781420079333 and ISBN-10: 1420079336), written by authors Torsten Hothorn, Brian S. Everitt, was published by Chapman and Hall/CRC in 2009. With an overall rating of 4.3 stars, it's a notable title among other Applied (Mathematics) books. You can easily purchase or rent A Handbook of Statistical Analyses Using R, Second Edition (Paperback) from BooksRun, along with many other new and used Applied books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.58.

Description

A Proven Guide for Easily Using R to Effectively Analyze Data

Like its bestselling predecessor, A Handbook of Statistical Analyses Using R, Second Edition provides a guide to data analysis using the R system for statistical computing. Each chapter includes a brief account of the relevant statistical background, along with appropriate references.

New to the Second Edition

  • New chapters on graphical displays, generalized additive models, and simultaneous inference
  • A new section on generalized linear mixed models that completes the discussion on the analysis of longitudinal data where the response variable does not have a normal distribution
  • New examples and additional exercises in several chapters
  • A new version of the HSAUR package (HSAUR2), which is available from CRAN

This edition continues to offer straightforward descriptions of how to conduct a range of statistical analyses using R, from simple inference to recursive partitioning to cluster analysis. Focusing on how to use R and interpret the results, it provides students and researchers in many disciplines with a self-contained means of using R to analyze their data.

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