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Statistics : An Introduction Using R by Michael J. Crawley (2014, Trade Paperback)
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Statistics : An Introduction Using R, Paperback by Crawley, Michael J., ISBN 1118941098, ISBN-13 9781118941096, Brand New, Free shipping in the US For students of science, engineering, medicine, or economics in their first or second undergraduate year who have little or no background in mathematics or statistics, Crawley introduces the essentials of statistical analysis using the powerful open-source statistical language R. People versed in statistics could also use th to learn R. The topics include dataframes, variance, analysis of covariance, multiple regression, binary response variable, and death and failure data. Annotation ©2015 Ringgold, Inc., Portland, OR ()
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About this product
Product Identifiers
PublisherWiley & Sons, Incorporated, John
ISBN-101118941098
ISBN-139781118941096
eBay Product ID (ePID)201714420
Product Key Features
Number of Pages368 Pages
LanguageEnglish
Publication NameStatistics : an Introduction Using R
SubjectProbability & Statistics / General
Publication Year2014
TypeTextbook
Subject AreaMathematics
AuthorMichael J. Crawley
FormatTrade Paperback
Dimensions
Item Height0.7 in
Item Weight24.1 Oz
Item Length9.4 in
Item Width6.6 in
Additional Product Features
Edition Number2
Intended AudienceScholarly & Professional
LCCN2014-024528
Dewey Edition22
IllustratedYes
Dewey Decimal519.5
SynopsisA revised and updated edition of this bestselling introduction to statistical analysis using the leading free software package R In recent years R has become one of the most popular, powerful and flexible statistical software packages available. It enables users to apply a wide variety of statistical methods, ranging from simple regression to generalized linear modelling, and has been widely adopted by life scientists and social scientists. This new edition offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a wide range of disciplines. Step-by-step instructions help the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material such as t tests and chi-squared tests, intermediate methods such as regression and analysis of variance, and more advanced techniques such as generalized linear modelling. Numerous worked examples and exercises are included within each chapter. Comprehensively revised to include more detailed introductory material on working with R Updated to be compatible with the current R Version 3 Complete coverage of all the essential statistical methods Focus on linear models (regression, analysis of variance and analysis of covariance) and generalized linear models (for count data, proportion data and age-at-death data) Now includes more detail on experimental design Accompanied by a website featuring worked examples, data sets, exercises and solutions www.imperial.ac.uk/bio/research/crawley/statistics Statistics: An introduction using R is primarily aimed at undergraduate students in medicine, engineering, economics and biology but will also appeal to postgraduates in these areas who wish to switch to using R., "...I know of no better book of its kind..." (Journal of the Royal Statistical Society, Vol 169 (1), January 2006) A revised and updated edition of this bestselling introductory textbook to statistical analysis using the leading free software package R This new edition of a bestselling title offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a wide range of disciplines. Step-by-step instructions help the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material like t--tests and chi--squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling. Includes numerous worked examples and exercises within each chapter.
LC Classification NumberQA276.12.C73 2014
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