Confusing about applying weighted least square for constant fitting
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I'm now fitting a line with noise. My equation is to minimize
corresponding to equation
, then I have
and
with
data. I want to caculate the best y. The WSL gives
for the answer. But now my confusing is what is Y? Is this
, which means my code is
, then I have
and
data. I want to caculate the best y. The WSL gives (1) is the matrix with number 1. Is this right for me? or I should use other function such as fminsearch(I saw in the community, maybe it's still my missunderstanding)...Thanks
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X = ones(N,1)
W = diag(w)
Y = y
where y is the (Nx1) column vector of the measurements and w is the (Nx1) column vector of weights.
The result of your formula is the coefficient a of the line y=a that best approximates the measurements.
5 Kommentare
WeiHao Xu
am 19 Jul. 2021
Torsten
am 19 Jul. 2021
The result is a number since you do constant fitting (you want to fit your vector y to a horizontal line y = a).
It is this number a that you get from the formula.
WeiHao Xu
am 19 Jul. 2021
Torsten
am 19 Jul. 2021
Correct.
WeiHao Xu
am 19 Jul. 2021
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