Dear all
I am pretty sure this is something that has been discussed
previously, but I was not able to find anything helpful. I'd like to
fit a polynomial to my experimental data. The data have errors, and
I'd like to use these errors as weights for the data values in the
fit. Something like this:
x = [0:10]; % x values of experimental data
y = x.^2; % y values of experimental data
y_err = randn(size(x)); % errors of y
[p,s] = polyfit (x,y,2); % <-- replace this by something that
takes into account the errors (y_err), e.g. using the weights 1./
y_err for each value in y
Any hints or ideas?
Matthias