Interpreting the Summary table from OLS Statsmodels | Linear Regression

In this video, we will go over the regression result displayed by the statsmodels API, OLS function. We will go over R squared, Adjusted R-squared, F-statistic & T-test of the feature values.

If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those.

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[joemireku]

I am glad you created this video 4 years ago.

[suhailchougle]

It's like the concepts which I couldn't find anywhere else on youtube, I end up here. This is exaclty what I was looking for, the explanation of this "ols.summary()".I believed I wouldn't find any. But here I am. I really really appreciate this buddy .Thank you so much.

[dhananjaykansal]

This is just too good Bhavesh bhai. Please keep this work going. It's so helpful that I can't put it into words. Thanks a lot.

[deepshah]

Just what I was looking for!! Well explained 🔥🔥🔥🔥

[jorgemarin]

Thank you Bhavesh!! Just what I was looking for and very well explained.

[ruteshrathod]

thanks, Bhavesh Sir, for making concept clear about feature selection

[fahimmuntasirniloy]

Thanks for the quick and concise explanation.

[abhijeetpatil]

Thanks Bhavesh for such a beautiful video

[SHUBHAMKUMAR-tybh]

Bhavesh you are great. Very nice interpretation.

[victorperezandres]

Amazing video. Really helpful. Simple & clear. Thanks a lot!

[souhamahmoudi]

Thank you very much for this tutorial, it's really hepful !

[areejawanareejawan]

It's really amazing thanks for such informative and highly understandable videos.

[daveneifer]

Thanks so much for the breakdown of the results table, it was very helpful.

[SaffronKage]

Super thanks such a simple and accurate explanation being from programming background the stats summary interpretation was bugging me a lot

[tejasbiraris]

This was quite helpful. Thankyou so much

[robertomatsumotocobra]

Thank you very much, it was really elucidative!

[mobbie]

Thanks great explanation - you're the best!

[lokeshrathi]

You making these videos as early as 6:47 AM shows your hard work and passion for this!
Thank you so much for your Work!

Question:" The condition number is large, 2.9e+04. This might indicate that there are
strong multicollinearity or other numerical problems."
- what do you infer from this?

[SumitKumar-uqdg]

Hi Bhavesh. Really a big fan of ur videos. U have made all topics so easy to understand. Can u pls make a video on evaluating a logistic regression model with statistics like ks test, psi, concordance pls

[ice-skully]

Thank you for the explanation, though I have an issue with understanding why we should omit feature 3, it is the only feature with a high p-value and therefore fails to reject the null coef = 0 hypothesis meaning that there is a linear relationship between it and the target.