I am running a zero inflated negative binomial model (zinb) and want to interpret the main and interaction effects.
I have the following:
People decide whether to purchase a good during a given week and I have their final purchase quantity (Min qty = 0 units and Max qty = 6 units observed from the data). Assume I am looking at the results for a particular week. I have multiple demographics variable but two discrete variables are imp namely:
education (=1 if buyer has college degree, 0 otherwise) and gender (1 = female, 0 = male).
Due to the presence of a large number of 0's and overdispersion, I use a zinb model.
# The code I use on Stata (providing the code so that people can look up # the regression equation): zinb purchase_qty i.gender##i.educ, inflate (c.age)
The regression results are (I'm not presenting results for the inflation part here):
purchase_qty Coef. P>|z| 1.gender -0.26 0.028 1.education -0.07 0 gender##education 1 1 0.12 0.027 _cons -0.5 0.56
What I want to calculate from the above table is the average qty purchased by the four groups:
(gender=0 & educ=0,gender=1 & educ=0,gender=0 & educ=1 and gender=1 & educ=1).
Could someone please explain how to do this from the above output(taking into account that main effect and interaction effect are significant)?
P.S. Please let me know if you need any clarification from my side.
Edit: I had used the wrong dependent variable initially which I changed later.
Edit 2: Inflation part results -
inflate Coef. P>|z| age -0.6 0.015 _cons 0.9 0