You might be aware that wiggly functions can be well-approximated by polynomial models. But sometimes polynomials are not good enough, or maybe you already know the functional form of the model you want to fit. In these cases, nonlinear least squares regression can be a good method.
Through a simple example, you learn how the Nonlinear platform finds the optimal values of the model parameters. We discuss the algorithms available and how to choose one, as well as defining the function through the model library or saving your function to your model library. Multiple examples are used to illustrate the concepts. An overview of the Fit Curve platform is also included.
