An introduction to applied multivariate analysis with R / Brian Everitt, Torsten Hothorn.Material type: TextLanguage: English Series: Use R!Publication details: New York : Springer, c2011. Description: xiv, 273 p. : ill. ; 24 cmISBN: 9781441996497; 1441996494; 9781441996503; 1441996508Subject(s): Multivariate analysis -- Data processing | R (Computer program language) | Multivariate Analyse | R (Programm)DDC classification: 519.535 LOC classification: QA278 | .E87 2011Other classification: ST 601 | SK 830 Online resources: WorldCat details | Contributor biographical information | Publisher description | Table of contents only | Ebook Fulltext
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|E-Book||EWU Library E-book||Non-fiction||519.535 EVI 2011 (Browse shelf(Opens below))||Not for loan|
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|519.535 ACD 2013 Discovering structural equation modeling using Stata /||519.535 AGC 2013 Categorical data analysis /||519.535 BYS Structural equation modeling with AMOS :||519.535 EVI 2011 An introduction to applied multivariate analysis with R /||519.535 FIA 2007 The analysis of cross-classified categorical data /||519.535 HAM 2015 Multivariate statistics :||519.535 HID 2008 Design and analysis of experiments.|
Includes bibliographical references (p. 259-269) and index.
TOC Multivariate 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.
"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.