03853cam a22005417a 45000010005000000030009000050050017000140080041000310100017000720200018000890200015001070200018001220200015001400350021001550400037001760410008002130500021002210820022002420840016002640840016002801000027002962450095003232600034004183000034004524900011004865040064004975050318005615201421008795260007023005900016023076500051023236500041023746500038024156500029024537000029024828300018025118560142025298560106026718560091027778560092028688560052029609420014030129990015030269990015030419990015030569520133030719520107032046707BD-DhEWU20181205105439.0110405s2011 nyua g b 001 0 eng d a 2011926793 a9781441996497 a1441996494 a9781441996503 a1441996508 a(OCoLC)728101887 aLTSCAcLTSCAdCDXdBD-DhEWUbeng aeng00aQA278b.E87 201104a519.535bEVI 2011 aST 6012rvk aSK 8302rvk1 aEveritt, Brian.91958113aAn introduction to applied multivariate analysis with R /cBrian Everitt, Torsten Hothorn. aNew York :bSpringer,cc2011. axiv, 273 p. :bill. ;c24 cm.1 aUse R! aIncludes bibliographical references (p. 259-269) and index.0 tTOCaMultivariate data and multivariate analysis -- Looking at multivariate data: visualisation -- Principal components analysis -- Multidimensional scaling -- Exploratory factor analysis -- Cluster analysis -- Confirmatory factor analysis and structural equation models -- The analysis of repeated measures data. a"The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data."--Publisher's description. aAS aTahur Ahmed 0aMultivariate analysisxData processing.919582 0aR (Computer program language)91958307aMultivariate Analyse.2swd91958407aR (Programm)2swd9195851 aHothorn, Torsten.919240 0aUse R!919520423WorldCat detailsuhttp://www.worldcat.org/title/introduction-to-applied-multivariate-analysis-with-r/oclc/728101887&referer=brief_results423Contributor biographical informationuhttp://www.loc.gov/catdir/enhancements/fy1304/2011926793-b.html423Publisher descriptionuhttp://www.loc.gov/catdir/enhancements/fy1304/2011926793-d.html423Table of contents onlyuhttp://www.loc.gov/catdir/enhancements/fy1304/2011926793-t.html423Ebook Fulltextuhttp://lib.ewubd.edu/ebook/6707 2ddccTEXT c6707d6707 c6707d6707 c6707d6707 00102ddc40718NFICaEWUbEWUcREVd2015-05-13eParamag4195.80l0o519.535 EVI 2011p26674r2015-05-26tC-1w2015-05-26yTEXT 00102ddc40708NFICaEWUbEWUcEBOOKd2018-01-03l0o519.535 EVI 2011r2018-01-03w2018-01-03yEBOOK