Abstract
Abstract An attempt is made to predict phosphate load by means of discriminant analysis. Eight groups of data are defined by a cluster analysis. Principal component analysis and an F-ratio of the predictor variables are used to find a most favourable group of predictor variables by which an optimal separation between the eight different groups of data is possible. The discriminant functions, linear combination of the predictors, together with additional help of a classification procedure like Euclidic distances may be used to assign an individual phosphate measurement to the group it best corresponds to. The discriminant analysis shows that a linear combination of two out of a total of 33 predictors: namely the combination of (1) runoff and (2) settlement area have the best discriminant power. Multivariate tests of significance are performed. Tables are constructed that demonstrate the predicted versus actual group membership. Phosphate load along with the predictor variables were measured from 1973 to 19...
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