Weighted polyfit?

[Matthias Brennwald]

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