Hi all, here's a question that's been bugging me lately: How important is statistical significance of coefficients in "predictive modeling"?
That is, let's say one is attempting to predict response to a marketing campaign using a logistic regression, and produces two models. The first model predicts 76% of cases correctly, and has some coefficients that are statistically significant and a number that are not statisticall significant. The second model, on the other hand, contains only variables that are statistically significant, but predicts only 61% of cases correctly. For the purposes of prediction only � one does not care at all about hypothesis testing for any of the coefficients in the model � which is a "better" model and why? Furthermore, where can I read up to get a better grasp on this? . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
