Method of least squares | Lecture 13 | Vector Calculus for Engineers

The method of least-squares is derived for fitting a line to experimental points by setting partial derivatives equal to zero.
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[@ProfJeffreyChasnov]

Find other Vector Calculus videos in my playlist th-cam.com/play/PLkZjai-2JcxnYmkg6fpzz4WFumGVl7MOa.html

[@parth.mandaliya]

Thank you. This was very helpful.
Can you please put the link to the video where you are doing that matrix calculation?

[@SF-zm2py]

So squaring is used as opposed to absolute value (or even 4th power) since it makes taking the partials much easier. Neat. But why are we only considering the vertical offset to the line for each point instead of its perpendicular offset? Is it because we only assume that there is noise in the 'y' measurement? Would we have to use a different method of there was noise for both 'x' and 'y'?

[@moviemusicvideochannel7102]

thanks so many thanks, I have finally understood least square

[@黄媚-o2y]

we can use cramers rule to solve that system of linear equations and the ans could be seen with bare eyes

[@forheuristiclifeksh7836]

0:04

[@qurbanalizangi8522]

Sir bs mathematics student 👦📖🎒

[@makayacine9314]

My brain hurts.