# What statistics can I apply to explain how much socio demographics influence revenue?

I'm looking to explain the revenue made by a coffee shop based on socio demographic data like gender, population, education and stores nearby. I've seen that by applying a logistic regression to revenue using those as explanatory variables could work but I don't finalize to understand how it is that I can give out number and compare each one of those variables to say which generates more revenue, could I apply a logistic regression to explain how much influence each one of variables selected have? And how? Also seen that Random Forest might be useful for it but I don't finish to get what it is and how can I apply it in this context?

• a) Because I saw a couple of tutorials make it dichotomic, as saying more than 50 grand or less. But I actually want to keep it continuous. b) Yes indeed, I just want to know which demographic aspects influence more on final revenues for the shop. Gamma makes the best sense but glm library is not available for R-Studio 3.6, so it turns into a stack overflow question. Thanks! – Melania CB Jan 22 at 14:41