What are Assumptions of Linear Regression? Easy Explanation for Data Science Interviews

In this video, we’ll go over the four assumptions of linear regression. Interviewers frequently ask questions about linear regression, so we’ll dive into both the assumptions themselves, as well as how to diagnose violations of the assumptions. I’ll also offer insight into which assumptions are critical versus which are less important.
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Contents of this video:
====================
00:00 Introduction
00:43 The Assumptions
01:38 Things To Note
02:04 Assumption #1
02:50 How To Diagnose
03:16 Residual Plots
03:48 Assumption #2
04:17 How To Diagnose
04:52 Assumption #3
05:19 Violations of Normality
05:57 Q-Q Plots
07:12 Summary of Q-Q Plots
07:24 Assumption #4
07:55 How To Diagnose
08:15 Residual Plots
08:53 To Summarize

[@MinhNguyen-lz1pg]

Very useful for MLE Interview! Thanks Emma :)

[@youngzproduction7498]

Nice tips. Thanks a lot.🎉

[@evasun531]

Awesome video!

[@oguedoihuchigozirim7257]

I have not finished this video but this is the best I have seen so far. Though you didn't talk about multicollinearity, everything here is so clear in simple English Thank You!

[@Levy957]

What about multicolinearity ?

[@shilashm5691]

It is assumption of Ordinary Least Square(OLS),not assumption of linear regression!!!

[@devanshverma5395]

How are they different?

[@shilashm5691]

@@devanshverma5395 because, we can use other least square method like total least square, partial least square in linear regression. So we cannot say it as assumption of linear regression, we should say assumption of ols, other least square methods has their own assumptions!

[@firesongs]

what about features are uncorrel with the error term (iid) and features are uncorrel with each other (no multicollinearity)?

[@venkateshgunda25]

Correct me if I am wrong, but how are features un-correlated with the error term useful for a Model? Which would mean no matter what we do to a particular feature weight, the error term cannot be controlled with it as it is iid wrt to the feature. So, we might as well remove it from our Model 😅
Isn't the second assumption more applicable for Naive Bayes? I am not sure if Linear Regression is especially sensitive if this assumption does not hold true, as it would just mean switching the signs & values of weights and make the correlated features converge towards a lower error. I mean, it would mean less overall information, but it probably does not affect the performance negatively.

[@shilashm5691]

@@venkateshgunda25 If features are correlated with the error(a.k.a residuals), it means using the features we can able to predict the error, if a model can predict the error, then it means it does overfitted, always our model should only learn the signal not the noise.
Refer to GAUSS MARKOV THEOREM

[@xiaofeichen5530]

Her second point says "residuals are independent". So we can deduce that features are not correlated with the errors.

[@XinJin-zf1zo]

@@xiaofeichen5530 Yes. Error must be independently otherwise it violates the first assumption with linearity.