Polynomial fitting with all fitting feedback (chi-squared, etc.)

Hi, I’m familiar with ‘’‘PolyFit’‘’ for fitting polynomials to data, but that algorithm just returns the best-fit polynomial coefficients. Is there any easy way to automatically get back more of the useful fitting results info – (especially) chi-squared, plus maybe reduced chi-squared, residuals from the fit, etc. – without having to manually put all of that together? It seems like someone would have written a function for all of that.

Also, while I’m here, would anyone suggest a smoother fitting strategy than simple polynomials? (Dealing with a simple data set of a ~dozen data points.) The goal is to find the best strategy that finds an interpolation that pays less attention to the few weird outliers. (I already tried Splines, but those were forced to actually go through each data point, even the bad ones.)


For your second question, Dierkckx provides “smooth” (non-interpolating) splines with an adjustable smoothness parameter.