I have two sets of data
y(x), one from experiments, and one from simulations. The datasets are naturally paired, as the same 80 different test set-ups were used for both data sets. I would like to quantify the difference between the data sets with a simple parameter.
Here is what my datasets might look like:
set1 = [ x1 y1 1.1 3.0 1.3 5.2 1.4 6.7 ... ] set2 = [ x2 y2 1.2 3.2 1.2 5.1 1.5 6.9 ... ]
Importantly, the x parameter has some dependence on the y parameter, which causes the values to be slightly offset in the x direction.
If the x values where the same, I would simply do something like calculating the average of
y1/y2 for all
x to be able to say "set 1 is in general z % greater than set 2". However, the
x offset complicates things.
I've looked at chi-squared test, Pearson correlation, and Euclidean distance, but I can't tell if they are applicable in my case.
For reference, here is a plot of the actual data. Each point in the left graph corresponds 1-1 to a point in the right graph.
Excuse me if the terminology is off here, I am a statistics novice.