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[post author here] FWIW I did not find your original comment particularly offensive, and didn't downvote (I think years on the internet have made me resilient)
by eliben 10y ago
[post author here]
FWIW I did not find your original comment particularly offensive, and didn't downvote (I think years on the internet have made me resilient).
The Gauss-Markov theorem is very cool. However, as I discovered while doing some reading for this article - whole books were written on linear regression. The amount of information to convey is almost limitless and I didn't want to turn this one into a multi-part article. I simply wanted to write down (code and math) the things I find most pertinent to understanding how the regression works.
- apathy 10y agoFair enough! If you felt like adding it as a footnote, great, if it doesn't fit the purpose, that's fine too. And to reiterate, it's a beautifully laid out, concise, high-content piece of writing. I should have phrased it differently; how it's said may matter more than what's said. Essentially I wrote a reaction post but, given the amount of revising and reanalysis/examination-of-assumptions I've been doing lately, I ought to have said something like: "This piece is an excellent tutorial on how to do linear regression, why gradient descent is fundamental (far beyond OLS or fixed effects models), and so forth. However, if you are interested in why all of these things are possible in the multivariate case as well as the simple bivariate case, you might find the Gauss-Markov theorem to be a 'light bulb' moment." Because that's how it happened to me. Cheers, and thanks for writing a great piece that I will be pointing others towards.
- eliben 10y agoThanks for the feedback and the useful reference. I'll definitely add the Gauss-Markov theorem to my to-look-in-depth-at-later list and may write up about it at some future point.