# Combining variables for logistic regression

Currently I'm working on a model that predicts sale based on the activity of a rep. It can be 3 types: mail, phone call, or meeting. These variables are continuous and somewhat cross-correlated, e.g., a rep can send 10 emails, make 1 call and have 3 meetings - and the more calls a rep makes, the less emails he/she sends.

Ultimately I'm trying to understand proportional weight of each activity to create combined activity metrics and say something along the lines: To win a deal with X% probability you need to perform 10 activity "units" where a meeting gives you 5 units, a call 3 units, and an email 1 unit.

It looks that in this case logistic regression should be used to estimate probability of sales from combined activity metric, but how to get best weights for each activity type?

$$P = \frac {e^{b_0 + b_1 X_1 + b_2 X_2 + b_3 X_3 + \dots b_p X_p}}{1 + e^{b_0 + b_1 X_1 + b_2 X_2 + b_3 X_3 + \dots b_p X_p}}$$