This is basically a compilation of the lecture notes I wrote when teaching
36-401, Modern Regression,
in fall 2015. I offer it here on the chance that it might be of interest
to those learning, or teaching, linear regression. There's no shortage of
resources on that, but I have tried to present the subject as though statistics
had made *some* progress since 1960, de-emphasizing bits of theory which
rely on Gaussian noise and correctly-specified linear models, in favor of
more computationally-intensive, but robust, techniques. If anything,
I did not go far enough.

The manuscript has some over-lap with Advanced Data Analysis from an Elementary Point of View (especially that book's second chapter, "The Truth About Linear Regression"), but also a lot of new and lower-level material. Comments and (especially) corrections are appreciated.

(Last text update: 17 February 2019)