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Exploratory multivariate analysis by example using R / François Husson, Sébastien Lê, Jérôme Pagès.

By: Husson, FrançoisContributor(s): Lê, Sébastien | Pagès, JérômeMaterial type: TextTextLanguage: English Series: Chapman & Hall/CRC computer science and data analysisPublication details: Boca Raton : CRC Press, c2011. Description: xii, 228 p. : ill. ; 25 cmISBN: 9781439835807 (hardback); 1439835802Subject(s): Multivariate analysis | R (Computer program language)DDC classification: 519.538 LOC classification: QA278 | .H87 2011Online resources: WorldCat details | Ebook Fulltext
Contents:
TOC Principal component analysis (PCA) -- Correspondence analysis (CA) -- Multiple correspondence analysis (MCA) -- Clustering
Summary: Summary: "An introduction to exploratory techniques for multivariate data analysis, this book covers the key methodology, including principal components analysis, correspondence analysis, mixed models and multiple factor analysis. The authors take a practical approach, with examples leading the discussion of the methods and lots of graphics to emphasize visualization. They present the concepts in the most intuitive way Read more...
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
E-Book E-Book Dr. S. R. Lasker Library, EWU
E-book
Non-fiction 519.538 FRE 2011 (Browse shelf(Opens below)) Not for loan
Text Text Dr. S. R. Lasker Library, EWU
Reserve Section
Non-fiction 519.538 FRE 2011 (Browse shelf(Opens below)) C-1 Not For Loan 27413
Text Text Dr. S. R. Lasker Library, EWU
Circulation Section
Non-fiction 519.538 FRE 2011 (Browse shelf(Opens below)) C-2 Available 28175
Total holds: 0

"A Chapman & Hall book".

Includes bibliographical references and index.

TOC Principal component analysis (PCA) --
Correspondence analysis (CA) --
Multiple correspondence analysis (MCA) --
Clustering

Summary:
"An introduction to exploratory techniques for multivariate data analysis, this book covers the key methodology, including principal components analysis, correspondence analysis, mixed models and multiple factor analysis. The authors take a practical approach, with examples leading the discussion of the methods and lots of graphics to emphasize visualization. They present the concepts in the most intuitive way Read more...

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