
Interpretation of Coefficientfree Models Bruce Ratner, Ph.D. The ordinary regression model is the thought of reference when database marketers hear the words "new kind of model." Data analysts use the regression concept and its prominent characteristics when judiciously evaluating an alternative modeling technique. This is because the ordinary regression paradigm is the underpinning for the solution to the ubiquitous prediction problem. Marketers with limited statistical background undoubtedly draw on their educated notions of the regression model before accepting a new technique. New modeling techniques are evaluated by the coefficients they produce. If the new coefficients impart comparable information to the prominent characteristic of the regression model  the regression coefficient  then the new technique passes the first line of acceptance. If not, the technique is summarily rejected. A quandary arises when a new modeling technique, like some machine learning methods, produce models with no coefficients. The primary purpose of this article is to present a method for calculating a quasiregression coefficient, which provides a frame of reference for evaluating and using coefficientfree models. Secondarily, the quasiregression coefficient serves as a trusty assumptionfree alternative to the regression coefficient, which is based on an implicit and hardlytested assumption necessary for reliable interpretation. 1 800 DM STAT1, or email at br@dmstat1.com. 
