
Data defines the model by dint of genetic programming, producing the best decile table.


Building a Database Model to Assess the True Impact of a Test Campaign Bruce Ratner, Ph.D. 

At the beginning of everyday for the regression modeler, whose tasks are to predict a continuous outcome (e.g., profit) and a binary outcome (e.g., yesno response), the ordinary least squares (OLS) regression model and the logistic regression model, respectively, are likely to be put to use, giving promise of another workday of successful models. The modeler is unwittingly assessing the model against the chance model, namely, a randomly selection of individuals. A stronger model would be one that assesses the true impact of a test campaign. The purpose of this article is to introduce such a model  the genetic GenIQ TAC Model (test and control), which builds a model, for either outcome type, that guarantees to assess the true impact of a test campaign a given a control campaign. Two cases studies are discussed.

For more information about this article, call Bruce Ratner at 516.791.3544 or 1 800 DM STAT1; or email at br@dmstat1.com. 
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