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Unformatted text preview: nt pathologists routinely undertake various activities in order to describe and predict (i) disease
risks based on climatic variables or changes in host plant and/or
pathogen populations, (ii) the impact of anthropogenic activities
(farming practices such as cropping systems and management
schemes) on the magnitude of diseases, and (iii) preference or
perception of producers on new products and technologies for
disease management. These activities entail collection of data on
many variables. With the development of computers and automatic digital data recorders, the amount of data and number of
variables are increasing dramatically. Plant pathologists working
in areas such as epidemiology, pathogen ecology, pathogen population biology, and disease management are challenged in uncovering patterns in multivariable data.
Statistical methods are available for analyzing data comprised
of multiple variables (7,9,11,18), and encompass three major
tools (Table 1): (i) ordination, which aims at describing data by
identifying a reduced data dimension of a few variables that account for the greatest amount of variability in the data; (ii) discrimination, which aims at delineating experimental groups or
classifying observations into experimental groups based on a set
of variables; and (iii) canonical, which aims at describing and
predicting the relationship between two sets of variables.
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This document was uploaded on 11/20/2013.
- Summer '11
- The American, Multivariate statistics, MANOVA