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An R Companion to Applied Regression Paperback – Jan 26 2011


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Product details

  • Paperback: 472 pages
  • Publisher: Sage Publications; 2nd ed. edition (Nov. 29 2010)
  • Language: English
  • ISBN-10: 141297514X
  • ISBN-13: 978-1412975148
  • Product Dimensions: 17.8 x 2.7 x 25.4 cm
  • Shipping Weight: 748 g
  • Average Customer Review: Be the first to review this item
  • Amazon Bestsellers Rank: #462,281 in Books (See Top 100 in Books)
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Product description

Review

"The text is very clearly written. It contains much wisdom and useful hints for those trying to analyze data with R."--Robert W. Hayden

About the Author

John Fox received a BA from the City College of New York and a PhD from the University of Michigan, both in Sociology. He is Professor Emeritus of Sociology at McMaster University in Hamilton, Ontario, Canada, where he was previously the Senator William McMaster Professor of Social Statistics. Prior to coming to McMaster, he was Professor of Sociology, Professor of Mathematics and Statistics, and Coordinator of the Statistical Consulting Service at York University in Toronto. Professor Fox is the author of many articles and books on applied statistics, including \emph{Applied Regression Analysis and Generalized Linear Models, Third Edition} (Sage, 2016). He is an elected member of the R Foundation, an associate editor of the Journal of Statistical Software, a prior editor of R News and its successor the R Journal, and a prior editor of the Sage Quantitative Applications in the Social Sciences monograph series.


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Amazon.com: 3.8 out of 5 stars 14 reviews
Sitting in Seattle
5.0 out of 5 starsExcellent, not only for regression models but for R in general
February 23, 2012 - Published on Amazon.com
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5.0 out of 5 starsThe associated car package is clearly very useful for fitting and evaluating models in R
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3.0 out of 5 starsGood but probably harder than necessary
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OUAGANET
5.0 out of 5 starsExcellent reference book in Regression using R programming
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Seth M. Spain
5.0 out of 5 starsUseful tool to learn R for real data analysis
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