I am running a regression model to predict dropout from an online program. People have to take 5 classes but some people dropped before taking the 5 courses. So I am using a dummy variables that is 1 if the person took that course or 0 if didn't take it. So the model looks like this:
Call: glm(formula = dropped ~ gender + region + level_of_education + SC0x_taken *enroll_time_SC0x + SC1x_taken * enroll_time_SC1x + SC2x_taken * enroll_time_SC2x + SC3x_taken * enroll_time_SC3x + SC4x_taken * enroll_time_SC4x + SC0x_taken * verify_time_SC0x + SC1x_taken * verify_time_SC1x + SC2x_taken * verify_time_SC2x + SC3x_taken * verify_time_SC3x + SC4x_taken * verify_time_SC4x + SC0x_taken * tot_video_hours_SC0x + SC1x_taken * tot_video_hours_SC1x + SC2x_taken * tot_video_hours_SC2x + SC3x_taken * tot_video_hours_SC3x + SC4x_taken * tot_video_hours_SC4x + SC0x_taken * tot_pp_hours_SC0x + SC1x_taken * tot_pp_hours_SC1x + SC2x_taken * tot_pp_hours_SC2x + SC3x_taken * tot_pp_hours_SC3x + SC4x_taken * tot_pp_hours_SC4x + SC0x_taken * Grade_SC0x + SC1x_taken * Grade_SC1x + SC2x_taken * Grade_SC2x + SC3x_taken * Grade_SC3x + SC4x_taken * Grade_SC4x + SC0x_taken * missed_assignments_SC0x + SC1x_taken * missed_assignments_SC1x + SC2x_taken * missed_assignments_SC2x + SC3x_taken * missed_assignments_SC3x + SC4x_taken * missed_assignments_SC4x + Order, family = "binomial", data = train)
I used the dummy variables so if the person has not taken the course, that value becomes zero and thus does not affect the model. But in the result, I have too many NAs and it says "not defined because of singularities"