computing error on least square fitting
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Hi, i slove a system of equations (Ax-b) using least square method. i get an output with x like [2.5; -11.1; 0.8; 0.5]. the status flag is zero with system converging at iteration 2 and relative residual of 0.019. I want to calculate the error on my fit i--e with which certainity my solution is accurate. Can i claim that the residual which is norm of (Ax-b)/b means that my fit has an error of 1.9%?if not how can i calculate error on my fit?
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Bruno Luong
el 15 de Ag. de 2020
Can i claim that the residual which is norm of (Ax-b)/b
No make it
norm(A*x-b) / norm(b)
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Bruno Luong
el 15 de Ag. de 2020
If you want an unnambiguous mathematical statement, just state exactly what mean:
norm(A*x-b) / norm(b) is approximatively 0.019
At your place I would say in the speaking language
The fit has a relative l2-norm residual of 1.9%.
The fit error usually designates the difference between the true and the estimated fit (parameters). So to me you shouldn't use the word "error."
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