This program provides two alternative Bayes solutions to problems of classifying an individual into one of K mutually exclusive populations on the basis of measurements taken on p predictor variables. It is assumed that the individual must have come from one of the K populations and must be assigned to one of them. Two simplifying assumptions are made. First, the p measurements are assumed to have a multivariate normal distribution in each of the populations.' Secondly, all misclassification errors are considered equally costly. The Bayes decision rule minimizes the total probability of misclassification. In this procedure. an individual is classified by means of "discriminant scores," one for each of the K populations, resulting in the assignment of the individual to that population for which he has the largest posterior probability. Such a Bayes procedure requires the "prior probabilities" that an individual, drawn at random, belongs to a given population. This procedure does not, however, require that the covariance matrices of the K populations be equal (homogeneous); a test of the homogeneity assumption is made by the program. If they are equal, the discriminant scores can be reduced to linear functions of the predictor variables. They are therefore called "linear discriminant scores." When the covariance matrices are unequal, the discriminant scores are quadratic functions and are called "quadratic discriminant scores." Thus, the mathematical form of the discriminant scores differentiates two types of Bayee procedures-linear and quadratic-both of which are provided by the program. For detailed discussions, see Anderson (1958), Rao (1965), and Fu1comer (1970). Method. (1) Notation. The following terms are used in this program description: K = number of populations, p

The purpose of this study was to determine the efficiency of a system containing aptitude, achievement, and noncognitive data, and a measure of college performance (first-semester grade point average) in predicting the longrange educational-vocational decisions of engineering students. What are the relevant sets of predictors when academic status is defined according to (a) persisters in engineering, transfers from engineering, and university withdrawals; (b) engineering subfields; or (c) distinct major fields of study? Noncognitive data best predicts between intellectually homogeneous student groupings, whereas first-semester grade point average dominates the predictor set relevant to more intellectually heterogeneous groupings. Concerning the discriminant procedures, their results were similar except for a decision rule that used sample proportions as estimates of prior probabilities.

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