# Interpreting Nonlinear Regression Results

Nonlinear regression problems often present computational difficulties:

- The choice of initial values for the parameters influences convergence. Try to choose initial values that are reasonable and, if possible, close to the expected final solution.
- Sometimes one algorithm performs better than the other on a particular problem. In the Options dialog, select the other algorithm if it is available. (If you specify a loss function or certain types of constraints, you cannot use the Levenberg-Marquardt algorithm.)
- When iteration stops only because the maximum number of iterations has occurred, the "final" model is probably not a good solution. Select Use starting values from previous analysis in the Parameters dialog to continue the iteration or, better yet, choose different initial values.
- Models that require exponentiation of or by large data values can cause overflows or underflows (numbers too large or too small for the computer to represent). Sometimes you can avoid these by suitable choice of initial values or by imposing constraints on the parameters.