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, produces models with no coefficients. The primary purpose of this chapter is to present a method for calculating a quasi-regression coefficient, which provides a frame of reference for evaluating and using coefficient-free models. Secondarily, the quasiregression coefficient serves as a trusty assumption-free alternative to the regression coefficient, which is based on an implicit and hardly-tested assumption necessary for reliable interpretation.