At this point you might as well use a polynomial class that can
accomodate a variety of bases for the space of polynomials - X^n,
(X-a)^n, orthogonal polynomials (translated and scaled as needed),
what have you.
I think I vote for polyfit that is no more clever than it has to be
but which warns the user when the fit is bad.
What about including multiple algorithms each returning a figure of fit?
Then I could try two or three different algorithms and then use the one that works best for my data.
Greg
--
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