Nonlinear regression minimizes the sum of the squares of the residuals; the residuals are the differences between the measured value and the value predicted by the model.

In general, nonlinear regression is preferred over linear regression.

When parameters are determined by nonlinear regression, their confidence limits must be determined in order to determine how close the measured values are to the true values.

From studying this module, you should now be able to:

Apply nonlinear regression to obtain values of parameters and their 95% confidence intervals.