The best way to decide between models is to use AIC and BIC. Let's focus mainly on AIC because BIC is very similar. If we have a collection of candidate models, the best model should minimize AIC. We want models to predict data well but adding too many features leads to overfitting. AIC rewards accuracy but punishes complexity, so it helps to choose models that balance these competing desires. So, let's start working with AIC and BIC, and explore how they can help us to predict models.
I'm going to fit another model, which doesn't include the features that are not statistically different from 0. We can do this using the following code:
This results in the following output:
So, here is the new model, where we have ...
