# Measuring the Importance of Variables in Multiple Regression (1 of 3)

When predictor variables are correlated, as they normally are, determining the relative importance of the predictor variables is a very complex process. A detailed discussion of this problem is beyond the scope of this text. The purpose of this section is to provide a few cautionary notes about the problem.

First and foremost, correlation does not mean causation. If the regression coefficient for a predictor variable is 0.75, this means that, holding everything else constant, a change of one unit on the predictor variable is associated with a change of 0.75 on the criterion. It does not necessarily mean that experimentally manipulating the predictor variable one unit would result in a change of 0.75 units on the criterion variable. The predictor variable may be associated with the criterion variable simply because both are associated with a third variable, and it is this third variable that is causally related to the criterion. In other words, the predictor variable and this unmeasured third variable are confounded.