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Least square method

in regression line y = mx + b why m = this ¥¥ SumOf(x - Xbar)*(y - Ybar) m = ____________________________ SumOf(x - Xbar)² how this formula come ? and why this is equal to m ?

19th Sep 2020, 3:10 PM
Rajababu Shah
Rajababu Shah - avatar
8 Answers
19th Sep 2020, 3:13 PM
Rohit
+ 4
yes m is the slope of the line but why that is equal to this formula ? RKK Mikhail BroFar JUMP_LINK__&&__Python__&&__JUMP_LINK [ Send Py Storms ] A J #Level 20 Atlas Hamington 🐉SHAROF🐉🇺🇿 sir, anyone please help
19th Sep 2020, 3:13 PM
Rajababu Shah
Rajababu Shah - avatar
+ 4
Coder Kitten how to take partial derivatives
21st Sep 2020, 8:31 AM
Rajababu Shah
Rajababu Shah - avatar
+ 4
Coder Kitten message me why df/dy = 2xy ?
21st Sep 2020, 8:40 AM
Rajababu Shah
Rajababu Shah - avatar
+ 3
why the formula of m is this ? BroFar i have learned m = y2-y1/x2-x1 m = N Σ(xy) − Σx ΣyN Σ(x2) − (Σx)2 (N is the number of points.) Intercept b: b = Σy − m ΣxN
19th Sep 2020, 3:40 PM
Rajababu Shah
Rajababu Shah - avatar
+ 3
You have some data points and want to find a regression curve that describes these set of points "adequately". So you need a method to get the parameters which summarize the data. A least square method is one way getting the parameters. As the name says, it aims at minimizing square (deviation). However there are multiple least square methods. This is why you might encounter different formulas.
19th Sep 2020, 7:37 PM
Lisa
Lisa - avatar