This is a follow-up question to a similar question asked weeks ago. Let say you used a set of historical data of customers confirmed enrollments (dependent variable) to series of products that your organization provide. The model that was developed using several inputs variables (independent variables) for example, number of orders, order type, product ordered, products added to cart, call log frequency, call type, call recency, refer a friend (RAF), RAF Recency, # Pageviews, Time on Page (seconds), page view product type, # clicks, # Times promoted by Email, # Times promoted by Direct Mail, # Contribution (Donation), Donation Type, Donation $, Donation Recency, etc.
Thus, # confirmed enrollments is a function {Var 1, Var 2, Var 3, Var 4, Var 5, Var 6, Var 7, Var 8, Var 8, Var 10, etc}.
At the end of the model development process, I saved the prediction formula to the data table used to developed the model. Thus, the prediction formula is predicting # confirmed enrollments as described above.
Now, I want to use this model to score new data table with similar variables. My question is, should the new data table have # confirmed enrollments in the table, or should the table only include those variables that are listed in the prediction formula? What I have done is that since the prediction formula is predicting # confirmed enrollments, which actually contains coefficient estimates for each of those variable and the response in the prediction formula, having # confirmed enrollments in the new data table that is to be scored using the prediction formula would auto correlate.
I just want to make sure that I am doing the right thing. Any expert advice would be appreciated.
Jenkins
