Thanks a lot! I have an exercise to do about this theorem and I didn't even knew we had that in our lecture, or what this all is gonna be about. you explained it very well. Thanks!!!

what about the proof for beta 0

Hi. Just a question. I got confused between removing variables on expectation and on a variance. Is it only in a variance that when we move out a variable from its expectation condition we need to square it? But if we only moved out from the expectation, we do not have to square the variable? I hope you understand my question. I was referring to 4:18 of this video when x was moved out from the expectation condition because it is conditional on x. Then, when moved out, it was not squared. So squaring the variable is only applicable if we have a variance.

is there any simple way to shorten this process? I am sure R must have many functions such as OLS or step.

Thank you, this video still helps after 8 years🥹

You are really good 😮😮. I can't express myself because I have exams next week and you just simplified it with ease and I also learnt with ease

4) On the basis of the information given below answer the following question( 5points) ΣΧ12= 6400 ΣΧ1Χ2 = 8600 ΣΧ2=800 ΣX22 = 7300 ΣΧ1y= 8400 ΣΧ2Y=27000 ΣY= 1600 ΣX1= 500 n=50 Σy12=56,000 a. Find the OLS estimate of the slope coefficient b. Compute variance of B̂2 c. Test the significant of B̂2 slope parameter at 5% level of significant d. Compute R2 and Ṝ and interpret the result e. Test the overall significance of the model

thank you!

Thank you

Thank you for this

GREAT

7:21 Did you mean Yi here? Instead of Y1

Is the sum of the error term the same as the expected value of the error term, since both equal zero?

6:52 Is the bar notation here the same as E[], i.e., the expected value?

Excellent presentation and clear! Thanks. Is there a way of including AIC?

My listening skill is not well, please adding the subtitle so that i can understand it more

Thank you so much! Merci infiniment pour votre clarté absolue.

Could you please do this in matrix form?

Why did you assume \sum(x_i - x_bar) = 0 in @5:00 but then take a conditional expectation of the same variables in @8:00 ? I would have just assumed they both equal to zero and have the \sum(xi-x_bar)^2 cancel. You would be left with beta_1_hat = beta_1. Great video either way. I am just not sure why you didn't assume \sum(x_i - x_bar) = 0 for both cases.

I am so upset for finding your videos late

I dont know what I am gonna pass the exam

Wait, after we get sum z_i*(y_i - y-bar) = n_1(y-bar_1 - y-bar), we can similarly get sum z_i*(x_i - x-bar) = n_1(x-bar_1 - y-bar) - Why don't we just cancel out n_1? In this way we will get beta = y-bar_1 - y-bar / x-bar_1 - x-bar. This must be wrong (because otherwise y-bar_0 = y-bar and x-bar_0 = x-bar), but I don't see why we cannot cancel out n_1 at this stage.

To find Beta 2 Hat is it the exact same method as finding Beta 1 Hat? But Step 1 would instead be: xi2 = a0hat + a1hat * xi1 + ri2hat

Good explanation and video!!

this is top tier explanation

the last section of 'Note', why we take the mean value instead?

Thank you so much!!!!

Bro have an exam tomorrow and you made it super simple to revise...! thank you

Why Yi hat does not include ui hat

Hey I think I got lost from 6:36 onwards

Really clear explanation. Thanks man!

Liked and fully watched!! This channel desperately needs smzeus!!

Why the sum of (xi-x(bar)) is constant? Here, xi takes different values!! And why you take conditional expectation?

Its a good presentation

Can you tell me between 9 and 17 minutes please

Could you please tell me after the 9th minute?

why the residual of partialling out not equal the residual of ols ,whereas the residual of fwl = residual ols

You teach really well! Thanks a lot!

Maybe it is better to mention upfront that you have assumed X as stochastic in the proof.

Thank you so much for the amazing video. The book completely skipped this explanation part which drove me crazy. Finally understood with your explanation.

I still don't get it I guess I'll watch it 10 more times :(

This has contributed a lot to my understanding. Thank you

You legend, I hope you're still teaching.

Thank you so much for this, much easier to understand than my lecturer 👍

Amazing video g

CAN'T BELIEVE THIS WAS SO SIMPLE

Thanks for showing this derivation step by step！

Anyone from ETC 2410 here? 😅

Just wanted to say that I really appreciate all your efforts for putting these up, it's helping students even 7 years later, thank you :)

Thanks a lot