In some situations you may wish to fit different models to different portions of your data. This is called segmental regression. This often occurs in kinetic experiments where you add a drug or perform some sort of intervention while recording data. The values collected before the intervention follow a different model than those collected afterwards.
If you didn't perform an intervention at a particular time along the X axis, it is unlikely that this kind of segmental regression is the analysis of choice.
Although Prism has no built-in way to fit different equations to different portions of the data, you can achieve that effect using a user-defined equation containing the IF function.
In this example, you collected data that established a baseline early in the experiment, up to "Start". You then added a drug, and followed the outcome (Y) as it increased towards a plateau. Prior to the injection, the data followed a horizontal line; after the injection the data formed an exponential association curve.
Y2=BASELINE + SPAN*(1-exp(-K*(X-START)))
It is easiest to understand this equation by reading the bottom line first. For X values less than START, Y equals Y1, which is the baseline. Otherwise, Y equals Y2, which is defined by the exponential association equation.
This equation has two intermediate variables (Y1 and Y2). Prism can fit the four true variables: START, SPAN, K, and BASELINE.
In many cases, you will make START a constant equal to the time of the experimental intervention. If you want Prism to fit START, choose an initial value carefully.
This kind of model is most appropriate when X is time, and something happens at a particular time point to change the model. In the example above, a drug was injected at time=Start.
The Range tab of the nonlinear regression dialog lets you define an X range that determines which points are fit and which are ignored.