I have built a logistic regression model to predict the probability that the event for my dataset occurs.
I also want to know the predicted amount (a regression problem) associated with that event (a customer buying my product).
Incumbent with the logistic model is including event and non-event outcomes. Is the same true for a regression (not classification) problem? Should I include in my linear model customers who didn't buy another product (and thus spent 0 dollars)? Or would that be sort of "double-dipping" if I went to multiply the likelihood * amount for an expected value of a given customer?