Variance of OLS estimators - part one

This video is the first part of the derivation of the variance of OLS estimators under the assumptions of the Gauss-Markov theorem. Check out ben-lambert.com/econometrics-... for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: ben-lambert.com/bayesian/ Accompanying this series, there will be a book: www.amazon.co.uk/gp/product/1...

[@yikunzhou7255]

Thanks a lot! This is exactly what I wont to find!

[@Adaeze8057]

Thanks a lot. Clearly, explained

[@aaronvarner3020]

Love your videos!! Quick question though: Why do you find the conditional variance of beta_hat given x_i, instead of just the unconditional variance of beta_hat?

[@richardlau898]

Thank you so much

[@paulfaulker4201]

Where is the beta hat formula in 4:01 came from? Which video mentions it?

[@LordOfNoobstown]

This may be late but its from his proof that beta^ is unbiassed. Meaning that the expected value of beta^ equals the population parameter beta. Video is here: ruclips.net/video/_L-TxH40H70/видео.html
edit: This formula is in the proof when you do not apply the expected value because if you would, the zero conditional mean assumption holds and the term to the right of beta p vanishes - he explains this in part 2 if i remember correctly

[@AI-ew1rj]

what's sxx^2 in the denominator?

[@xcrpt]

x-xbar squared

[@MaryjaneGermaniq]

@@xcrpt I think it is: sum of (x-xbar)^2

[@paulfaulker4201]

Do you happen to know where is the beta hat formula in 4:01 came from? Which video mentions it?
Thanks