Like polyfit.m but includes weighting of each data point.

Summary
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Find a least-squares fit of 1D data y(x) with an nth order polynomial, weighted by w(x).

Usage
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P = polyfitweighted(X,Y,N,W) finds the coefficients of a polynomial P(X) of degree N that fits the data Y best in a least-squares sense. P is a row vector of length N+1 containing the polynomial coefficients in descending powers, P(1)*X^N + P(2)*X^(N-1) +...+ P(N)*X + P(N+1). W is a vector of weights. Vectors X,Y,W must be the same length.

Class support for inputs X,Y,W:
float: double, single

By SS Rogers http://ssr.phy.umist.ac.uk