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:
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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

[oguedoihuchigozirim]

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!

[MinhNguyen-lzpg]

Very useful for MLE Interview! Thanks Emma :)

[shilashm]

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

[firesongs]

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