Hi, I'm currently using scipy.optimize.leastsq to do a fit, to get to the covariance matrix, I use self.s_sq = (infodict['fvec']**2).sum()/(self.Max_Dist_Between_Dips-(2*(max(self.Channel_List)-1)+1)) self.pcov = cov_x*self.s_sq wherein self.Max_Dist_Between_Dips = Number of Datapoints and (2*(max(self.Channel_List)-1)+1) the number of free parameters. This is according to the documentation, I believe (and one question here, https://mail.scipy.org/pipermail/scipy-user/2013-March/034316.html ) My (probably stupid) question is the following: As I understand, cov_x is the unscaled Hessian, right? Shouldn't I have to invert it at some point, to get to the Covariance? My guess would have been something like self.pcov = self.s_sq*np.linalg.inv(cov_x) Big thanks in advance for any hint, Udo