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

On relationship between regression models and interpretation of multiple regression coefficients