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.
Baird, Grayson L. and Bieber, Stephen L.
"The Goldilocks Dilemma: Impacts of Multicollinearity -- A Comparison of Simple Linear Regression, Multiple Regression, and Ordered Variable Regression Models,"
Journal of Modern Applied Statistical Methods:
1, Article 18.
Available at: http://digitalcommons.wayne.edu/jmasm/vol15/iss1/18