# Weight variables for predictive model

I received a question today that I wasn't exactly sure how to answer.

I have built a predictive model using a fairly basic logistic regression that works pretty well and fits our business needs. Recently, we purchased a CRM tool that allows us to build "probability" scores, but only allows the end users to give integer weights to various factors. Said differently, one can arbitrarily assign a weight of 10 points to one factor and -5 points to another with the sum of all weights representing the "probability" for a given entity in our database.

What I am looking to do is translate my model to this new format such that the resulting score equals the calculated probability from my logistic model. This is not out of desire, but business needs.

Admittedly I am not sure how to use the calculated coefficients and "adjust" them to these requirements. What is the best approach, if any? General thoughts on how to assign statistically valid integer weights to business criteria given these constraints?

Any thoughts or insight will be very much appreciated.

• Take a look at the literature about propensity score match, Though I thinks you are talk about something different. May 26 '11 at 13:13
• It seems you are asking two things, am I right? 1) convert probabilities measured on a 0-to-1 scale to ones measured on your other scale that includes various integers, perhaps -30 to +30 or something like that. 2) You want to convert your logistic coefficients to linear weights that will work with the alternate system (something I doubt can be done well, btw, because in logistic regression what is linear is the relationship between the predictor and the logit of the outcome). May 30 '11 at 17:08
• @rolando2 Yes, mostly correct. I need the end score to be 0-100 like a probability and the weights that calculate this score to be integers. That's the gist of it. At the end of the day, if I don't use my model, I am fine with that, but is there any statistically based method that fits these criteria? I am wondering if this "feature" was created and marketed without a statistical foundation. Jun 1 '11 at 15:40
• @Manoel: Can you clarify? I know PSM and nothing he says rings a bell about PSM for me. It sounds to me like his CRM software has a poor understanding of statistics and probability. Aug 2 '11 at 3:47
• On a second thought, I guess it has nothing to do with PSM... I thought for a moment that mabye the weights were some sort of propensity score, but I don't think so. Aug 2 '11 at 14:28