When computers do the calculating, however, it can matter. Calculation problems such as round off errors are far more likely when the values are very high or very low. We recommend that you scale your data to avoid values less than 10-4 or greater than 104. You lose information when you smooth data, and this won't get you a better fit. If you are fitting data to a sigmoidal dose-response or competitive binding curve, enter the X values as the logarithm of concentration, rather than the concentration itself.
Nonlinear regression is an iterative procedure.
It then adjusts these initial values to improve the fit. It then adjusts the values again and again until the improvement is tiny. You'll get the same answer no matter what initial values you use, unless the initial values are very far from correct. Initial values matter more when your data have a lot of scatter, don't span a large enough range of X values to define a full curve, or don't really fit the model. In these cases, you may get different answers depending on which initial values you use.