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I've got some simulation data which I'm trying to use to fit some historical data (red points). The simulation isn't a nice function $f(t, a, b, c...)$, instead it's just the result of numerically simulating a system. Is reasonable to take the nearest points in my simulation to each red point, and use these to calculate $\chi^2$ values to compare one simulation to another? Normally in physics we would use the number of parameters in the model to calculate reduced $\chi^2$ using degrees of freedom but I'm not really sure what my degrees of freedom are here. I use the initial and final condition so fix the bounds of my model, so perhaps $\nu = N_{\textrm{points}} - 2$?enter image description here

If $\chi^2$ isn't appropriate, is there some other way I can quantify the goodness of fit of my various simulations?

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  • $\begingroup$ "Quantify the goodness of fit" and using a chi-squared test are two (almost) completely different procedures: one is a scientifically meaningful, interpretable measure of agreement while the latter is a hypothesis test--all it can do is detect a discrepancy you can't attribute to chance. Which is the one you really want to do? BTW, the chi-squared distribution won't work here even if you did know the DF (unless you have many more data points): see my discussion at stats.stackexchange.com/a/17148/919 for an explanation. $\endgroup$
    – whuber
    Apr 20 at 15:37
  • $\begingroup$ Thanks, I think I might be getting confused. In physics we often use the $\chi^2$ goodness of fit parameter which is $\sum (O_i - E_i)^2 / \sigma_i^2$, where $\sigma$ is the uncertainty on observed results. Here I don't really have any uncertainties (I've got exact values for both my simulation and what I'm trying to recreate) so perhaps I've used the $\chi^2$ test parameter by mistake. Would you recommend using mean squared error in that case to quantify goodness of fit in this case? $\endgroup$
    – TIF
    Apr 20 at 15:41
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    $\begingroup$ That's one option. The choice depends on the physically right way to compare a point to the curve. For instance, if relative values are important, you might want to use the mean squared log error. $\endgroup$
    – whuber
    Apr 20 at 15:56
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One commonly used measure of fit is mean squared error: $$\frac{\sum{(f(x) -y)^2}}{N}$$

Sometimes this is a good representation of the business loss from error. Often it is used for the less good reason that it is easy to optimise, for some fitting functions $f$. But your $f$ seems to complicated.

Are you trying to fit some free parameters? If you simulations are differentiable, you could do it by stochastic gradient descent.

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  • $\begingroup$ Thanks, that's similar to what I've been doing already, just I've been doing $(y_i-f_i)^2/E_i$, and comparing those values between simulations, possibly without justification. I'm at least comfortable using MSE without having to justify it! My data comes from a complicated model for a physical system so I can't really optimise parameters. $\endgroup$
    – TIF
    Apr 20 at 15:33
  • $\begingroup$ If I can justifying using degrees of freedom like I mentioned then I'd like to use $\chi^2$ though as that's fairly common in physics $\endgroup$
    – TIF
    Apr 20 at 15:36

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