Decomposition of Variability: Sum of Squares | Statistics Tutorial

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In this tutorial, we will explore the determinants of a good regression - the sum of squares total, the sum of squares regression and the sum of squares error. We dig deep into the concepts to define their role within the regression model and look into how they are connected.
The sum of squares total (SST) is a measure of the total variability of the dataset, while the sum of squares regression (SSR) describes how well your line fits the data. The sum of squares error (SSE), on the other hand, is the difference between the observed value and the predicted value. And what is the connection between these three concepts? Watch the whole video to find out!
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#SumOfSquares #Statistics #DataScience

[@365DataScience]

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[@korchageen]

Probably the best visual explanation on the Internet for a non statistical person. Wish many person find this channel.

[@reuelkarani6284]

This is the most underrated video ever!!

[@RonMagsamen]

Short and precise 🫡

[@larissacury7714]

Loved this video! Do we have pre set parameters for these terms? Ex: what would be a good sum of squares error?

[@dewantahmid95]

you made it very very clear. thank you...

[@365DataScience]

You are welcome!

[@polarequatorial709]

Is it ok to speculate on what explains the residuals?

[@ojassaxena2891]

What is orthogonal sum of squares

[@mariosanchez5392]

my head is still scrambled lol

[@hariharansubramanian8988]

How is it a measure of dispersion or variability? Suppose, I have 2(upwards) as a difference(Yactual - Ymean = 2) and 3(downwards) as a difference(Yactual - Ymean = 3) . Then 2 power of 2 i.e 4 + 3 power of 3 i.e 9. That is, 4+9 is 13?! So, how does this imply variability or dispersion. Yeah. A lot of books talk about converting negative to positive. But that is not the correct logic. Errors are magnified, in fact, and may imply distortion.

[@timacorn2536]

You sound like this: 🤓

[@dukeworthington483]

Thanks for nothing. I got nothing

[@EdoardoMarcora]

This is plainly wrong! Regression does not imply causation