In statistics, a proper linear model is a linear regression model in which the weights given to the predictor variables are chosen in such a way as to optimize the relationship between the prediction and the criterion. Simple regression analysis is the most common example of a proper linear model. Unit-weighted regression is the most common example of an improper linear model.
Bibliography
- Dawes, R. M. (1979). "The robust beauty of improper linear models in decision making". American Psychologist. 34 (7): 571–582. doi:10.1037/0003-066X.34.7.571. S2CID 14428212.
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