I am doing a computer simulation for some physical system of finite size, and after this I am doing extrapolation to the infinity (Thermodynamic limit). Some theory says that data should scale linearly with system size, so I am doing linear regression. The data I have is noisy, but for each data point I can estimate errorbars. So, for example
Tag: least-squares
Orthogonal regression fitting in scipy least squares method
The leastsq method in scipy lib fits a curve to some data. And this method implies that in this data Y values depends on some X argument. And calculates the minimal distance between curve and the data point in the Y axis (dy) But what if I need to calculate minimal distance in both axes (dy and dx) Is there