Affiliations: [a] The Open University, Milton Keynes, MK7 6AA, UK | [b] University of Dhaka, Dhaka, Bangladesh
Correspondence:
[*]
Corresponding author: Paul H. Garthwaite, School of Mathematics and Statistics, Open University, Milton Keynes, MK7 6AA, UK. Tel.: +44 1908 655974; E-mail: [email protected].
Abstract: In applications of multiple regression, one of the most common goals is to measure the relative importance of each predictor variable. If the predictors are uncorrelated, quantification of relative importance is simple and unique. However, in practice, predictor variables are typically correlated and there is no unique measure of a predictor variable’s relative importance. Using a transformation to orthogonality, new measures are constructed for evaluating the contribution of individual variables to a regression sum of squares. The transformation yields an orthogonal approximation of the columns of the predictor scores matrix and it maximizes the sum of the covariances between the cross-product of individual regressors and the response variable and the cross-product of the transformed orthogonal regressors and the response variable. The new measures are compared with three previously proposed measures through examples and the properties of the measures are examined.
