I am very new to inferral statistics. I am trying to build a prediction model for forecasting the revenue for physicians based on some historical data. I was planning to use Multiple Linear Regression model where the Payment is dependent to predictors such as Number Of Patient Visits,Number Of Charges for that month.
The structure of the data looks like :-
Payment(Monthly) | Patient Visits | Charges Count | Month Date |
Now after I have built an regression model,I have to forecast the payments of the physician based on the model. But now due to extrapolation while doing the forecast I assume I cannot give values of predictors outside the range of data with which it was built. For example the range for patient Visits is from 100 to 1000,now I want to predict what will be the payment if I had 2000 patient visits. I am not getting correct results with the model that I have built.
One of the other thoughts that I have is building Time Series models. But in that case time will be in X axis and the Payments will be in the Y - axis and we will be looking at the trend of the payments over time to make future predictions. But I want to use the effect of other predictors also when making future predictions.
Please let me know how best I can achieve this. As I am new to this any guidance will be highly appreciated.
Thanks in Advance!!