Deriving the OLS Estimators in Simple Linear Regression Model - Part 1

[@derekstreidl3187]

This was a very clear way of explaining it! I pay thousands of dollars for professors to ignore my requests for help and could not care less if their students pass or not. You're a great teacher, unlike everyone at Krannert Management School of Purdue U. Thank you!!!!

[@helpmereachjust10msubs48]

U mean thousands of dollars for getting a degree? duh imagine if u start investing

[@shireenkhan6847]

I spent whole day trying to understand this but I finally did thanks to your video. Thank you so much

[@hopekivuyo8826]

I never thought that I would come to understand derivation of estimates easier like this

[@sushmithags4683]

Thanks a lot. It was easy to understand and explained really well. I appreciate it.

[@pruthvinbatham6137]

Probably the best video of this topic, thanks for this!

[@akshar2715]

Thank you! This video helped me understand with ease. Great explanation

[@AtsedeZinabu-dq4vm]

great work keep it up

[@aloybackbanerjeeup7634]

Nice and wonderful content. I would be very happy if you can calculate the coefficient in case of TLS or total least square method too

[@peacego624]

Man, thanks are not enough. But, thanks from the bottom of my harth.

[@ivioalves7228]

Your are a friend my friend!! Thank you!

[@dalkeiththomas9352]

absolutely amazing

[@1UniverseGames]

Can you please show one more thing of this calculation. Like how we can obtain intercept and slope of B0 and B1 after shifting line l to l'?

[@oghenefejiroagbi1469]

This video is so helpful and clearly explained. Thank you so much! But Please does anyone know why alpha hat a^ (which is not indexed by i) is in the summation but beta hat B^ gets taken out ?

[@MCxWillyxP]

I would also like to know this @techinsightsjournal

[@louie30003000]

Since it's a partial derivative with respect to â, b hat is held constant and can therefore be pulled out of the sigma.

[@hassanbolagligsman]

@@louie30003000 do you know why we put a focus with the partial derivative on alpha?

[@louie30003000]

@@hassanbolagligsmanSince the regression line y=a^ + b^x has two unknowns (the y-intercept a^ and the slope b^), to minimize the residual square function requires taking two partial derivatives: first with respect to a^ (where b^ is considered a constant), and then with respect to b^(where a^ is considered a constant).

[@kerkebedeley]

It was adorable 😍😍 from Tigray

[@Lucyferandtheson003]

Thank you man, but why are we minimizing with respect to a and not x?

[@aimalkhan663]

Gr8 bro ❤❤❤

[@naturebedtime5156]

very helpful

[@mathiasmbendela7939]

It is good but indeed there are some areas that need to be further clearly explained just like what others have oberseved

[@abigaelmokeiramonii6390]

Nice job

[@siwedewadugna5002]

best videos teacing thankss

[@Economics365]

Why after 1/n multiplication that alpha and beta becomes their respective hats

[@searamanat8168]

Why did you multiply by 1/n?

[@mishka7930]

to remove the summation signs

[@ele4984]

I was beginning to think there is something wrong with my brain I understand this but in class I absolutely don't understand what those prof are doing and have to figure it out outside class.

[@Aaron-kv6vr]

thank you so much

[@Akshay-cj3hq]

but why is alpha an inner function hmm

[@jujum4851]

Thank you

[@victoriabrimm5014]

THANK YOU

[@aslay1064]

if you divide the first derivative by 2, should the answer be (yi-a^-b^xi)/2 = 0 You just casually got rid of the 2 and the minus sign?? 🤔

[@projectnew9172]

helpful but your volume is low

[@1UniverseGames]

Sir, great video. I need a little help, I have a question which I'm not understanding properly. Can you make a video of it to explain it to us. I can share the question to you, if you provide me your email address. It will be really helpful. Thanks in advance