The Separation Principle in Linear Regression


text may be freely shared among individuals, but it may not be republished in any medium without express written consent from the authors and advance notiication of the editor. Abstract In linear regression problems in which an independent variable is a total of two or more characteristics of interest, it may be possible to improve the t of a regression equation substantially by regressing against one of two separate components of this sum rather than the sum itself. As motivation for this \separation principle," we provide necessary and suucient conditions for an increased coeecient of determination. In teaching regression analysis, one might use an example such as the one contained herein, in which the number of wins of Major League Baseball teams is regressed against team payrolls, for the purpose of demonstrating that an investigator can often exploit intuition and/or subject-matter expertise to identify an eecacious separation.

Cite this paper

@inproceedings{Samaniego2007TheSP, title={The Separation Principle in Linear Regression}, author={Francisco J. Samaniego}, year={2007} }