The Estimates at Each Iteration table shows the progress of the algorithm as it attempts to converge on a solution. Each row in the table is a nonlinear model. For each step, the table displays the parameter estimates and the sum of squared residuals (SSE). Iteration zero uses the starting values that you specified for the initial model. For each subsequent iteration, the algorithm adjusts the parameter estimates in a manner that it predicts should reduce the SSE compared to the previous iteration. The iterations continue until the algorithm converges (within the specified tolerance) on the minimum SSE, a problem prevents the subsequent iteration, or Minitab reaches the maximum number of iterations.
The Estimates at Each Iteration table is most useful when the algorithm fails to converge on a solution. For example, you can use the information in the table to identify parameters that do not stabilize to help focus your efforts to resolve the problem.