The Goldilocks Dilemma: Impacts of Multicollinearity -- A Comparison of Simple Linear Regression, Multiple Regression, and Ordered Variable Regression Models

Authors

Grayson L. Baird, University of WyomingFollow
Stephen L. Bieber, University of Wyoming

Abstract

A common consideration concerning the application of multiple linear regression is the lack of independence among predictors (multicollinearity). The main purpose of this article is to introduce an alternative method of regression originally outlined by Woolf (1951), which completely eliminates the relatedness between the predictors in a multiple predictor setting.

DOI

10.22237/jmasm/1462076220

Erratum

Table 2, entitled “Comparison of Simple, Multiple, and Ordered Regression when the predictors are related,” located on page 353 contains a transpose error under the subheading “R^2 Estimates.” Specifically, the R_Model^2 value for MLR should read “.165” instead of “.137.” Likewise, the R_Shared^2 value for MLR should read “.137” instead of “.165.”