MLE vs OLS | Maximum likelihood vs least squares in linear regression

See all my videos at: www.tilestats.com
At 9:03 I should have said 4.24 and not 4.25.
1. Ordinary least squares (0:30)
2. Maximum likelihood estimation (03:41)
3. Log-likelihood (10:45)
4. MLE vs OLS (11:42)

[@Mister_Merb]

This video was criminally low in my KZbin search results.

[@kozark875491]

The BEST video explaining what MLE is and how we use Normal Distribution! Really thank you!!! I'm gonna share the video a lot!!

[@user-wr4yl7tx3w]

really appreciate your presentation format. it's always super clear and leaves little room for ambiguity.

[@Daniboy370]

Maybe the best explanation I came across so far

[@thomasvaudescal5749]

Excellent explanations! Clear and easy to digest content.

[@irvingnino3422]

Bruh, it took me clicking on hundreds of videos to finally land on what I was looking for!

[@Justjemming]

Wow this is great. The visuals really help to reinforce what is the MLE equations. Thank you!

[@itamar.j.rachailovich]

This video is extremely good! You are great! you have new subscriber! keep going, well done!

[@ibntuahirabdulhaqq5866]

Wooow! It is the best lecture so far on this topic. Thanks greatly

[@dhanasekarankuppuswami6094]

Super! Excellent explanations! It is very nice and useful. Thanks.

[@reidl4767]

Fantastic video! Thank you :)

[@youngzproduction7498]

Great vid. your lesson saves my life :D. Thanks for your dedication.

[@nawarajbhujel8266]

Absolutely brilliant

[@mangaart3366]

Thank you for the tutorial! 🙂

[@brazilfootball]

I hope you will consider doing a video on explaining what a link function is in GLM's and why they are used. I'm having trouble grasping the concept and how/why it relates to means and the normal distribution, etc.. I'm enjoying your videos very much, thanks again!

[@tilestats]

Have a look at around 7 min in this video about Poisson regression kzbin.info/www/bejne/hZPTq5KLq9RlqLM

[@brazilfootball]

​@@tilestats Well, that was easy. Thank you! 😅

[@jaimehenriquez2985]

this is so awesome. ty!

[@sum1sw]

Thank you so much. Do you have an opinion on Orthogonal Distance Regression ?

[@mustafizurrahman5699]

Excellent

[@user-fw2kc6iv1f]

good explanation bro

[@thebjjtroll6778]

Please do a GLM series, Also the subtitles on the bottom really help. Thank you!

[@tilestats]

GLM MLE Logistic and Poisson regression: kzbin.info/aero/PLLTSM0eKjC2cYVUoex9WZpTEYyvw5buRc

[@catelinnx7677]

Thanks!

[@tilestats]

Thank you!

[@Leila0S]

Hello. I sent you a question on your website few days ago asking about availability of the content of Multivariate statistics course in PDF format, and haven't received any reply from your side so far. could you please inform whether you plan to make them available on-line and when is the proposed date for this?

[@tilestats]

Thanks for your interest. I will try to do that in 2-3 weeks.

[@Leila0S]

@@tilestats Thank you. It will really help as I have an exam of this course next month and your explanation is distinguished.

[@tilestats]

Hi If you go to my homepage www.tilestats.com there should be a link under "Shop" to Payhip where you can buy the full course as 32 PDFs. Let me know how it works.

[@Leila0S]

@@tilestats This is perfect. Thank you a million times. I have to inform you that I already bought 4 pdfs last week. Those that are related to Euclidean dustance and the PCA. Is there any chance to offset their price against the total price of the full Maltivariate course? I can share with you the paypal e-mails regarding the transfer of the 4-lectures price.

[@Leila0S]

@@tilestats I have purchased it. Thank you❤❤❤ Thanks a lot

[@celilylmaz4426]

Teşekkürler.

[@tilestats]

Thank you!

[@brazilfootball]

4:48 Is this the visualization of: “residuals are normally distributed” for a linear model?

[@tilestats]

Yes

[@brazilfootball]

@@tilestats Thank you

[@shawnkim6287]

Thanks for the video, TileStats. I just want to ask you if I understand the lecture video. First, to choose the right distribution, the distribution we choose such as normal distribution reflect the distribution of the target variable (i.e. data point). Second, I am referencing to another lecture you created (kzbin.info/www/bejne/jqG0oWqdhZt1pLM). The distribution of prediction errors (i.e. error = the difference between the y and y^) has to appear to be normally distributed, if you choose the normal distribution. If you are using binomial distribution, the distribution of prediction errors has to appear to be binomially distributed. Can you please confirm this? Thank you!

[@tilestats]

When I refer to the distribution in this case, I mean the distribution around the line (the distribution of the residuals). However, in the special case of binomial distribution, where the response variable, for example, only can take two values (0 or 1), we use logistic regression where the model is fitted based on the ML method. As an example where a normal distribution does not work, I suggest that you have a look at my video about Poisson regression and the videos about logistic regression: kzbin.info/www/bejne/hZPTq5KLq9RlqLM kzbin.info/www/bejne/r5nSmHd4esZjq7M kzbin.info/www/bejne/gGHcpn-raNR_q7c