Simply run the program. If the program is run with an empty ignored list, the result will show up as a bunch of NaNs. Do you recall that earlier we have done some correlation analysis on how some variables are correlated with one another?
We'll start by adding those into our ignored list, and then run the regression. Once we have a score that is no longer NaN, we can start comparing models.
The final model I have prints the following output:
R^2: 0.871 Variable Coefficient StdErr t-stat p-value Intercept: 12.38352 0.14768 83.85454 0.00000 MSSubClass_30: -0.06466 0.02135 -3.02913 0.00412 MSSubClass_40: -0.03771 0.08537 -0.44172 0.36175 MSSubClass_45: -0.12998 0.04942 -2.63027 0.01264 MSSubClass_50: -0.01901 0.01486 -1.27946 ...
