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Personally, I felt that the omission was rather important. When the first year's worth of many statistics programs can be condensed into one beautiful theorem,

by apathy 10y ago

Personally, I felt that the omission was rather important. When the first year's worth of many statistics programs can be condensed into one beautiful theorem, which is centuries old, I feel it's disrespectful to one's forebears to ignore it. However, I get the impression that once the meme sensor goes off, any links remain unvisited and reading comprehension may suffer. So I learned something here, too. I'd edit the original to remove the meme, but since I can't do that at this point, readers will have to try and control their Internet Police tendencies and read a few sentences. Or not. Rather a shame, hopefully the author of the piece will fix it and this will all be superfluous.

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 ago
  Fair 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 ago
    Thanks 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.
dguaraglia 10y ago
To be honest, the attitude doesn't help. Did the author omit something that you find important? Well, you can point it out saying something like "hey, nice informational article, but I would have included X, Y and Z." It's basically what you said, but in a more constructive manner.
- apathy 10y ago
  Mm, probably best if the next time I have that sort of urge, I try writing it up and see if what I'm suggesting is even compatible with what the author sought to accomplish. I momentarily forgot how difficult it can be to stay tightly focused on the parts that the author wants to emphasize. It's an important result, and hinted at directly by the derivation, but ultimately it's not my decision to make.

by apathy 10y ago

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 ago
Fair 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 ago
  Thanks 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.

dguaraglia 10y ago

To be honest, the attitude doesn't help. Did the author omit something that you find important? Well, you can point it out saying something like "hey, nice informational article, but I would have included X, Y and Z." It's basically what you said, but in a more constructive manner.

apathy 10y ago
Mm, probably best if the next time I have that sort of urge, I try writing it up and see if what I'm suggesting is even compatible with what the author sought to accomplish. I momentarily forgot how difficult it can be to stay tightly focused on the parts that the author wants to emphasize. It's an important result, and hinted at directly by the derivation, but ultimately it's not my decision to make.