Least Square Estimators Development using Matrices

[@PhysIQ]

This was a life saver…..finally someone explained the entire derivation steps! Was stuck at the 3rd last step for so long

[@giulialoura6622]

Video MARAVILHOSO. Salvou minha vida e explodiu meu cérebro!!!

[@suryamachani2417]

All I did after watching is just "hit the subscribe button"!

[@kevinciocco5876]

Great video, thanks 👍🏻.

[@RyeCA]

This is so helpful, thank you so much

[@abc5202171]

Very good explanation! Thank you!!!!

[@albertansahbenson6431]

You are a genius

[@Stats4Everyone]

Haha! Def not. Tho thanks! Glad you found this video to be helpful:)

[@sali6989]

Perpect explanation...can you explain also the likelihood method estimation for logistic regression coefficient

[@Stats4Everyone]

Thank you for this comment. Yes, this is a good idea. I will make a video on this either later today, or tomorrow. I will respond with a link as soon as it is ready.

[@Stats4Everyone]

New Video on MLE for Logisitic Regression: kzbin.info/www/bejne/Z4q8mmami8ebrac

[@steffanseok9300]

how does taking the derivative in respect to b ensure the min of the residuals squared is given and not the max?

[@ngalahassan1351]

Good question - to respond to this a second derivative will result into a [X^tX] which is always positive as long as X is non zero real number hence b is local min

[@florianwotawa7579]

In almost all videos on yt the explanation for the derivative of b‘x‘xb is „ah just think as if that is b^2” BUT that’s actually wrong, throughout your solution is right.

[@bognarpeti]

excellent, thank you! :)

[@Stats4Everyone]

Glad it helped!

[@ludovickei2335]

Like it, thanks!

[@Stats4Everyone]

Thanks for the feedback! I'm happy to hear that you found this video to be helpful!

[@houssemghazala]

Thank you

[@jbetanco7733]

thank you