228 pages, 87 figs, 50 tabs
Full of real-world case studies and practical advice, this book focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis when variables are quantitative, correspondence analysis and multiple correspondence analysis when variables are categorical, and hierarchical cluster analysis. The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. All of the data sets and code are available on the book's website.
It is an excellent book which I would strongly recommend as a secondary text, supporting or accompanying the main text for any advanced undergraduate or graduate course in multivariate analysis. ! this is a compact book with a plethora of visualizations teaching all subtleties of major data exploratory methods. It would supplement well any primary textbook in an advanced undergraduate or graduate course in multivariate analysis. --MAA Reviews, July 2011 ! a truly excellent [chapter] on clustering ! is an example of what upper-division undergraduate writing should aspire to. ! this enjoyable book and the FactoMineR package are highly recommended for an upper-division undergraduate or beginning graduate-level course in MVA. The acid test for such a work must be whether it is likely to spark an interest in students and prepare them adequately for more detailed, serious study of the subject and this book easily passes that test. --Journal of Statistical Software, April 2011, Vol. 40
Principal Component Analysis (PCA). Correspondence Analysis (CA). Multiple Correspondence Analysis (MCA). Clustering. Appendix. Bibliography of Software Packages. Bibliography. Index.
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Francois Husson is an assistant professor of statistics at Agrocampus Ouest in France. Sebastien Le is an assistant professor of statistics at Agrocampus Ouest in France. Jerome Pages is a professor of statistics and head of the applied mathematics department at Agrocampus Ouest in France. They are all developers of the FactoMineR package dedicated to multivariate exploratory data analysis.