Fit 1D polynomials to data using min chi^2 (least squares)
`
`

`
Usage: ($yfit, [$coeffs]) = fitpoly1d [$xdata], $data, $order, [Options...]
`

`
`

`
Signature: (x(n); y(n); [o]yfit(n); [o]coeffs(order))
`

Uses a standard matrix inversion method to do a least
squares/min chi^2 polynomial fit to data. Order=2 is a linear
fit (two parameters).

Returns the fitted data and optionally the coefficients.

One can thread over extra dimensions to do multiple fits (except
the order can not be threaded over - i.e. it must be one fixed
scalar number like 4).

The data is normalised internally to avoid overflows (using the
mean of the abs value) which are common in large polynomial
series but the returned fit, coeffs are in
unnormalised units.

`
`

`
$yfit = fitpoly1d $data,2; # Least-squares line fit
($yfit, $coeffs) = fitpoly1d $x, $y, 4; # Fit a cubic
$fitimage = fitpoly1d $image,3 # Fit a quadratic to each row of an image
$myfit = fitpoly1d $line, 2, {Weights => $w}; # Weighted fit
`

`
`

`
Options:
Weights Weights to use in fit, e.g. 1/$sigma**2 (default=1)
`