Explaining linear regression

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Explaining basic interpretations to linear regression and how to do a hypothesis test with it

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The name "Least Squares Regression" comes from the fact that the OLS estimators are the solution to a minimization problem. The objective for this minimization is the sum of the squared error. So minimizing this sum gives the smallest (least) squared error.

In fact most of the more advanced ML models are based on an extension of linear regression. Most of these don't have an analytic solution like ordinary least squares, but we can solve for the parameters using gradient descent (or variations).

berjonah

Really enjoyed this video. Great refresher to watch even if you are already familiar with linear regression

MattiasL

💙. Thanks again for your nice videos :) Will you also make one about multiple linear regression? I am particular interested in the necessary preconditions for those models, like, for example homorscedacity.

EDIT: I should probably wait with those questions until I finish the video

Unaimend

This is a great teaching aid for linear regression. One thing though: in your formulation of the normal equations the X is transposed; it should be (X X^T)^{-1} X Y if you're solving X^T \beta = Y.

AtticusRex-tg

woops: 6:18 slight typo in treatment (treamtent in the slide)
also, love your channel keep up the good work 👏🏼👊🏼

jaopredoramires

7:24 every other relation, that's monotonically increasing: am I a joke to you?

johannbauer

Reading about the bases of least squares lately, trying to understand what motivates some researchers to modeling the noise with Gaussian Processes (context of Bayesian Regression). Does anyone has any insight?
Nice video coming at the right time !

Eloss