I've been doing fits to lowest sum of squared relative error for a while now. These are useful when a data set exhibits increasing heteroscedasticity as the values of the independent variable increase, i.e., data scatter proportional to distance along the x axis. You can test this at http://zunzun.com by selecting a fitting target of "Lowest sum of squared relative errors" when fitting, and this is also in the Python Equations package at http://sf.net/ptojects/pythonequations. Having investigated fit statistics for some time now, it seems *everything* is geared toward absolute error, with not the slightest drop that I can find regarding relative error. These statistics are needed when performing SSQREL rather than SSQABS fitting. Can I safely use existing routines for covariance matrices and parameter standard errors simply by substituting dy(relative)/dx and relative error whenever dy(absolute)/dx and absolute error are used? I apologize, but this is over my head and I would like to report the fit statistics properly. James Phillips http;//zunzun.com P.S. I don't yet have much in the way of fit statistics on the web site, this is what I'm currently working on. I found much of what I need in the BSD-style licensed MPFIT.py at http://cars9.uchicago.edu/software/python/mpfit.html written by Mark Rivers.