  In this paper, we consider the problem of treating linear regression equation
coefficients in the case of correlated predictors. It is shown that in general
there are no natural ways of interpreting these coefficients similar to the
case of single predictor. Nevertheless we suggest linear transformations of
predictors, reducing multiple regression to a simple one and retaining the
coefficient at variable of interest. The new variable can be treated as the
part of the old variable that has no linear statistical dependence on other
presented variables.
