Some Aspects of Nonorthogonal Data Analysis

Abstract

It is known that regression results can be misleading when the predictor variables (x's) are highly correlated (nonorthogonal). The objective of this paper is to present some guidelines for deciding when the correlations among the x's are so large that the numerical accuracy and/or physical interpretation of regression results should be questioned. A measure of nonorthogonality is presented and the effects of correlated x's and poor model formulation on tho estimated coefficients are discussed. Emphasis is placed on the practical interpretation of regression results. Two illustrative examples are presented.