Chapter 2 M2: MLR mechanics

In this module we start to dig into how we actually fit linear regression models. What does “best fit” actually mean? Why is this a reasonable way to approach the problem? And what does all this look like with matrices involved?

Then, we venture into the world of frequentist inference, aka hypothesis testing. We’ll take a look at the general framework of this form of inference, and in particular, how it works in regression.

Some of these sets of notes come with a “response moment.” You don’t need to write down your answers to these (unless they also show up on the pre-class questions assignment), but it’s good to take a minute or so to think about them – they are designed to help you check your own understanding of what you’ve just seen.