This paper discusses briefly the classic multiple linear regression model and indicates the principal ways in which its assumptions are inadequately met when it is used as a model for prediction purposes in the social sciences. It also considers the damping effects of errors of measurement and of selective sampling on estimates of partial regression and multiple correlation coefficients and describes techniques whereby these effects may in part be overcome.