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Introductory statistics with R by Peter…
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Introductory statistics with R (edition 2008)

by Peter Dalgaard

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2623101,611 (3.64)2
This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.… (more)
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Title:Introductory statistics with R
Authors:Peter Dalgaard
Info:New York : Springer, c2008.
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Introductory Statistics with R by Peter Dalgaard

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I found this a helpful guide in using R to analyze and present data for a paper I was writing. I stopped reading when the paper was submitted, and I don't have anything else to compare it against, but if you need to use R to some end, I'd recommend it.
  pmorrison | Aug 30, 2006 |
Book Site
http://staff.pubhealth.ku.dk/~pd/ISwR.html

Runner up for Best Introductory Statistics book

Summary Review: This is the classic book introducing basic statistics using R, and it is still a great book for this purpose, exceeded only (in our opinion), by Statistics: An Introduction using R (2005) by Michael Crawley. ( )
  knol | May 13, 2008 |
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This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.

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