I've given a dataset of N samples like: (x,y) (-100,1), (-90, 30), (-50,60), (-10,90), (0,100), (10, 90), (30, 20),(100,4)
Now I would like to determine how well the data fits a perfect gaussian (normal) distribution. For that reason, I want to fit a gaussian curve to the data and in an additional step compare the estimated gaussian with the "perfect" gaussian. Any ideas how to achieve this in C++, Boost or Eigen functions?
I'm quite stuck here. Is this even a good approach? What I actually want is a measure of how well the data follows a gaussian distribution shape.