A Handbook of Statistical Analyses Using R, Second Edition （pdf）

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【资料名称】：A Handbook of Statistical Analyses Using R, Second Edition
【资料作者】：Brian S. Everitt, Torsten Hothorn
【出版社】：Chapman & Hall/CRC
Product Details

• Paperback: 376 pages
• Publisher: Chapman & Hall/CRC; 2 edition (July 20, 2009)
• Language: English
• ISBN-10: 1420079336
• ISBN-13: 978-1420079333
• Product Dimensions: 9.2 x 5.9 x 1 inches

Praise for the First Edition
…Brian Everitt has joined forces with a recognized expert who displays an impressive command of this powerful environment … Much is to be learned in the small details that make this text interesting even for experienced users. … Special attention is given to graphical methods …
—Journal of Applied Statistics, May 2007

Useful examples are presented to assist understanding. … Everitt and Hothorn have written an excellent tutorial on using R to analyze data using a wide range of standard statistical methods. … I highly recommend the text for anyone learning R and who want to use it for the sophisticated analysis of data.
—Joseph M. Hilbe, Journal of Statistical Software, Vol. 16, August 2006

… This book, using analyses of real sets of data, takes the reader through many of the standard forms of statistical methodology using R. … a very valuable reference. …The book is particularly good at highlighting the graphical capabilities of the language. …
—P. Marriott, ISI Short Book Reviews

…a useful, compact introduction.
—Biometrics, December 2006


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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