Curve fitting

Nonlinear regression is an important tool in analyzing data, but is often more difficult than it needs to be. No other program simplifies curve fitting like Prism. In fact, you can usually fit curves in a single step. Just select an equation from the extensive list of commonly used equations (or enter your own equation) and Prism does the rest automatically -- fits the curve, displays the results as a table, and draws the curve on the graph. Even better, Prism will automatically fit all related data sets at once. You don't have to repeat commands for each experimental condition.

Don't be fooled by the simplicity. Prism also gives you many advanced fitting options - automatically interpolate unknown values from a standard curve (i.e., to analyze RIA data), compare two equations with an F test or Akaike's Information Criterion (AIC), and plot residuals. Prism also lets you fit curves to a family of data sets at once, sharing the best-fit value of selected parameters to find one best-fit value that applies to the entire family, rather than individual best-fit values for each data set. Read more about global curve fitting - an incredibly versatile, but underused analysis technique.

The best way to learn nonlinear regression is to follow examples. The Prism 5 help system is a wonderful resource for learning about curve fitting. Our Guided Examples teach you how to think about nonlinear regression, as well as how to use Prism. All the examples use sample data built-in to the program, so you can easily work through the examples without any tedious data entry. Examples include fitting an enzyme kinetics curve, interpolating from a sigmoidal standard curve, global nonlinear regression and more.

Guided examples: Nonlinear regression