# $\chi^2$ parameter of a data fit

So what is that parameter. When I make a non-linear fit, the program gives me a value $\chi^2/doF$. What is it?

I know some statistics and I know those $\chi^2$ distributions are used for non-parametric contrasts, like how good a fit is to a data, knowing that $\lambda\rightsquigarrow\chi^2_{n-1}$ (that's supposed to mean the pearson parameter behaves like that distribution). I suspect it's totally related to that, telling me how good the fit is actually making the contrast, I don't know though, how to relate that value, as I'm not giving aconfidence level, so I guess it's a general number something independent of that so you decide if it's good or not, what is it exactly? Why is it divided by the degrees of freedom?

Could it be the value $x$ of that distribution in which our value of $\lambda$ falls for the null hypothesis of the data NOT following that fit, because looking at the results, that what makes most sense of what I've been thinking, as it looks like the bigger it is the better the fit is.

Thanks

No one?

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