[SciPy-user] Error in nonlinear least squares fit analysis

[David Lonie]

I got some help here earlier about finding a function to fit a
function to some exponentially increasing data. I have a few
questions:

a) fmin vs. leastsq:
The method I wrote ended up using the fmin() function to minimize the
error vector. What is the difference between fmin and leastsq? Is
there an advantage to using either?

b) Error in the parameters:
I'd like to know the precision that the fitted parameters are good to.
Basically, I'd like to know that b = 3.456 +/- 0.003 instead of just b
= 3.456.
leastsq can return a Jacobian matrix -- will pulling out the diagonal
elements of this matrix give me the results I want? Or is there a
better way?

TIA,

Dave