We have to confess that, until this point, we've delayed the crucial moment of truth when our linear model has to be put to the test and verified as effectively predicting its target. Up to now, we have just considered whether we were doing a good modeling job by naively looking at a series of good-fit measures, all just telling us if the linear model could be apt at predicting based solely on the information in our training data.
Unless you love sink-or-swim situations, in much the same procedure you'd employ with new software before going into production, you need to apply the correct tests to your model and to be able to anticipate its live performance.
Moreover, no matter your level of skill and experience with such types of models, you can easily be misled into thinking you're building a good model just on the basis of the same data you used to define it. We will therefore introduce you to the fundamental distinction between in-sample and out-of-sample...