Multivariate data analysis is based on two central

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Unformatted text preview: tivariate data analysis is based on two central features: (i) linear combinations of variables or variates, and (ii) distances or measures of association. In addition to these central features, Hair et al. (7) defined two notions that need to be considered in multiCorresponding author: S. Sanogo; E-mail address: [email protected] Publication no. P-2004-0719-02O © 2004 The American Phytopathological Society 1004 PHYTOPATHOLOGY portant variables that characterize the structure of an epidemic; and (iii) assess the potential of developing regional scale versus site-specific postmanagement schemes using weather and site variation. In all these cases, a simultaneous handling of several variables is required, and entails the use of multivariate statistics such as discriminant analysis, multivariate analysis of variance, correspondence analysis, and canonical correlation analysis. These tools have been used to varying degree in the phytopathological literature. A succinct overview of these tools is presented with cited examples. vari...
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This document was uploaded on 11/20/2013.

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