Linear Least Squares

Finding the line of best fit using the Linear Least Squares method.
Covers a straight line, parabola, and general functions.

[@julianzuloaga]

"Have fun programming this up", it's funny because that's exactly what I'm trying to do haha. Thanks Professor, your explanation really helped me!

[@extrastuff9352]

I bet there's a lot more people out there that could use this than have found it. Tis very well done.

[@AnkitVashisht]

Nicely explained professor, Respect from India :)

[@theae5570]

Thank you for your help!

[@yoseftadesse1377]

what a great explanation!!!!! thank you sir!

[@UmBr3ll4CorP]

Great explanation, but it would be more clear to show the derivation of the Least Squares Method over the actual minimization of squares of the residuals. That's why it's called least squared.

[@TheTranq]

I agree, but good explanation nonetheless. Better than what I got in class

[@AJ-et3vf]

@@TheTranq agree

[@AJ-et3vf]

Great video sir. Thank you!

[@zhao8275]

thank u so much for the brilliant explanation！！！

[@nikhil7455]

Great explanation , loved it ;)

[@Fahim18037]

Thank u sir..You helped me a lot

[@TPLCreationLoft]

If I'm understanding this correctly, this method solves 1 x column to a y column. In the polynomial case you can have n coefficients Cn, but still fitting that same x column to f(x). But what if you are trying to solve a polynomial for many independent variables xn?

[@sounavailable]

Is there a simple/intuitive explanation on why multiplying left with A transpose, even results in a system of equations who's solution is optimal? On a the surface, it only looks like a "trick" to make the over defined system of equations solvable

[@johnzhu5735]

probably would be more clean not to reuse 'm' to confuse people

[@PostcardProfessor]

Fair.