Abstract
The role of statistics is to summarize, simplify and explain the hidden features of a data set. When a large number of measurements are available, it is usual to enquire whether they could be replaced by a subset of the measurements, or of their functions, without loss of information. Principal Components, which are linear functions of the measurements, are suggested for this purpose. Two distinct procedures for the application of principal components to linear discriminant analysis have been proposed and investigated by Dillon et al. (1989) and Jolliffe et al. (1992 & 1996). Analysis of five case studies shows that one procedure achieves better linear discrimination than the other. Analysis also suggests that selected original variables are usually more discriminatory than selected principal components.
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