# Applied Multivariate Statistics with RDiscrimination and Classification

Applied Multivariate Statistics with R: Discrimination and Classification [IF WE HAVE multivariate observations from two or more identified populations, how can we characterize them? Is there a combination of measurements that can be used to clearly distinguish between these groups? It is not good enough to simply say that the mean of one variable is statistically higher in one group in order to solve this problem because the histograms of the groups may have considerable overlap making the discriminatory process only a little better than guesswork. To think in multivariate terms, we do not use only one variable at a time to distinguish between groups of individuals, but, rather, we use a combination of explanatory variables.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

# Applied Multivariate Statistics with RDiscrimination and Classification

29 pages

/lp/springer-journals/applied-multivariate-statistics-with-r-discrimination-and-5o3uTAr09O
Publisher
Springer International Publishing
© Springer International Publishing Switzerland 2015
ISBN
978-3-319-14092-6
Pages
257 –286
DOI
10.1007/978-3-319-14093-3_10
Publisher site
See Chapter on Publisher Site

### Abstract

[IF WE HAVE multivariate observations from two or more identified populations, how can we characterize them? Is there a combination of measurements that can be used to clearly distinguish between these groups? It is not good enough to simply say that the mean of one variable is statistically higher in one group in order to solve this problem because the histograms of the groups may have considerable overlap making the discriminatory process only a little better than guesswork. To think in multivariate terms, we do not use only one variable at a time to distinguish between groups of individuals, but, rather, we use a combination of explanatory variables.]

Published: May 22, 2015

Keywords: Support Vector Machine; Support Vector; Linear Discriminant Analysis; Regression Tree; Multinomial Logistic Regression

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