•Much easier interpolating. Many people use nonlinear regression for the purpose of interpolating values from standard curves. If that is your goal, most of the options provided in the nonlinear regression dialog are neither required or helpful. Prism 6 offers a new analysis just for interpolating curves. It offers only essential options, so is super easy to use.
•Plot functions to understand them. Before using an equation as a model to fit to data, it is wise to first understand it. A great way to do this is to plot the function and see how it changes when you alter the parameters. Prism plot functions using an analysis called Generate a family of curves. It addition to (or instead of) plotting the function itself, Prism 6 can now also plot the first and second derivative of the function, and its integral.
Fit more kinds of models with nonlinear regression
•Implicit and differential equations. It is no fun to struggle with algebra and calculus to express a model as an equation that defines Y as a function of X and parameters. It can even be impossible. Instead express your model as a differential equation (enter the equation that defines dY/dX) as or an implicit equation (an equation where Y appears on both sides of the equals sign).
•Write more complex models. Prism 5 let’s you define different models for different data sets by designating certain lines in the equation to only apply to a certain data set. For example, a line in the equation preceded with <B> applies only to data set B. Prism 6 extends this syntax to allow more complex equations. Precede a line with <A:D> for it to apply only to columns A-D, or with <A:K,2> to make it apply to every second data set between A and K (A, C, E, G, I, K).
•New weighting choice. When fitting a model to data with nonlinear regression, it is important to weight the data appropriately. This choice is often ignored, but it can affect the regression results. Prism always offered several choices for weighting. Prism 6 offers a new weighting choice, weighting by 1/YK. where K is a constant you enter on the weighting tab of the nonlinear regression dialog.
Additional results with nonlinear regression
•Hougaard's skewness. When you write a user-defined equation to fit to data with nonlinear regression, you can choose to express the parameters in multiple ways. For example, choose between fitting a rate constant or a half life. Hougaard's measure of skewness can help you choose the parameterization where the uncertainty is closer to symmetrical so the confidence intervals will be more accurate.
•Convert from linear to nonlinear regression with one click. Linear regression can be viewed a special case of nonlinear regression. Prism’s nonlinear regression analysis can be used to fit a straight line, and there are more than a dozen reasons why you might want to do so. But few people think of using the nonlinear regression analysis to fit a straight line. Prism 6 makes it easier to switch – simply click the “More choices” button on the linear regression dialog.
•Copy the linear regression equation. Prism always reported the best-fit values for the slope and intercept, but not in the form of an equation. Prism 6 now reports the equation for the line, ready to copy and paste onto a graph or into a manuscript.