simultaneous nonlinear regression of a lot of data

*To*: mathgroup at smc.vnet.net*Subject*: [mg65427] simultaneous nonlinear regression of a lot of data*From*: "dantimatter" <dantimatter at gmail.com>*Date*: Fri, 31 Mar 2006 06:09:24 -0500 (EST)*Sender*: owner-wri-mathgroup at wolfram.com

Hello all, I have a question about using the nonlinear regression function on a large data set. Perhaps some of you have suggestions, and can point me in another direction if this is not the best way to solve this problem. Basically I have ~100 data sets of ~200 points each, and I'd like to fit each set to the following function: G(t) = 1/N * [ y / (1+t/m1) + (1-y) / (1+t/m2) ] For each data set, the numbers N and y are different, but the numbers m1 and m2 are the same for all data sets. The problem is that I only know m1, and not m2. I am hoping to simultaneously solve all these data sets to come up with a value for m2, but I'm not entirely sure how to code it. I can come up with a reasonable m2 to start any regression. Any thoughts? Thanks!