# The robust beauty of improper linear models in decision making.

@article{Dawes1979TheRB, title={The robust beauty of improper linear models in decision making.}, author={Robyn M. Dawes}, journal={American Psychologist}, year={1979}, volume={34}, pages={571-582} }

Proper linear models are those in which predictor variables are given weights in such a way that the resulting linear composite optimally predicts some criterion of interest; examples of proper linear models are standard regression analysis, discriminant function analysis, and ridge regression analysis. Research summarized in Paul Meehl's book on clinical versus statistical prediction—and a plethora of research stimulated in part by that book—all indicates that when a numerical criterion…

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