Nonlinear regression starts with initial values for each parameter. If these initial values are very far from the correct values, nonlinear regression might go astray. To check this, go to theDiagnostics tab of the nonlinear regression dialog and choose the option at the top of the dialog: Don't fit a curve. Instead plot the curve defined by the initial values of the parameters.
If the curve defined by the initial values does not generally follow the shape of the data, and go near the data points, you should change the initial values (Initial Values tab of nonlinear regression) until they do.
Prism reports error codes as a single phrase that appears over the column of results, for example "Bad initial values" or "Impossible weights". Learn more about each error message.
Here is a short list of potential curve fitting problems with suggested solutions.
Try a different equation.
Enter different initial (estimated) values for the parameters. If you entered your own equation, check that you entered sensible rules for generating initial values.
If possible, collect more data. Otherwise, hold one of the parameters to a constant value.
Collect more data in the important regions.
Try to collect less scattered data. If you are combining several experiments, consider normalizing the data for each experiment to an internal control.
Use a simpler equation.
If your X or Y values are huge, divide by a constant to change the units. Avoid values greater than, say, 100,000.
If your X or Y values are tiny, multiply by a constant change the units. Avoid values less than about 0.00001.
Changing the units is unlikely to solve the problem, but it is worth a try.
Check that you haven't made a simple mistake like setting a maximum plateau to 1.0 when it should be 100, or a Hill slope to +1.0 when it should be -1.0.
Prism has three different analysis checklists for nonlinear regression. Use the one that matches your goal.