I have a very specific regression problem involving a data set with a general pattern of missing data. The data set has several variables and corresponding binary variables determining whether those variables are expected to have a value or not.
Basically if the binary variable has a value of 1, there would be a value (could potentially be zero, positive or negative) for the respective variable. If the binary variable has a value of 0, the corresponding variable would have no value. i.e. NULL or .
The structure of the data set corresponds to the real world scenario where some companies offer certain services while others do not.
An example dataset Dependent variable - Company Value (y1) Independent variable 1 - Total sales of product A (x1) Binary variable 1 - Does the company sell Product A (b1) Independent variable 2 - Total sales of product B (x2) Binary variable 2 - Does the company sell Product B (b2) y1 x1 b1 x2 b2 30 46 1 . 0 50 0 1 82 1 20 18 1 . 0 40 . 0 0 1
My actual data set consists of c.1000 records and there are 5 independent variables with their associated binary variables
Is it appropriate to incorporate all of the variables and binary variables into a single regression model and if so, do I add them in as separate terms or as interaction terms? There are potentially 32 combinations of product/service offerings in my data set so I don't see how I could easily split this into multiple models.