Expert Answer:
:
I thought I'll just elaborate a bit on what Aabaz said in case there are others who might find this useful (Although Aabaz's explanation is probably clear enough for people who have an understanding of the necessary math etc.).
First, I'm assuming you have a 2D plot but it shouldn't be difficult to generalize to ND case.
Basically, for each point in your experimental data (xi, yi), use your "theoretical curve" to estimate yi' for the value xi. This is probably what Aabaz is referring to by making grid step size the same so that you interpolate the points exactly at the x coordinate values xi of your experimental data using the formula for your curve(s).
Next, to measure whether the fitting is good, you could for e.g. measure the sum of square differences using:
error = sum( (yi' - yi)^2 ){where i range over all points in your exp. data}
Of course other error metrics other than least square could be used to estimate how well the data fit your model (i.e. your curve) but by far for most applications, least square is the most common.
Hope this helps.