Plot residuals and test them for normality
A residual is the difference between the actual and predicted value of Y. Prism 7 let you plot one kind of residuals from regression.
Prism 8 expands this to tabulate residuals also from ANOVA (one-, two- or three-way), t tests and multiple regression. For each of these analyses, test for normality of residuals in four ways and plot the residuals in four ways (including a QQ plot).
When the Y values represent an actual number of counts of objects or events (not normalized in any way), the residuals are not expected to follow a Gaussian distribution, but rather to follow a Poisson distribution.
Prism now lets you specify that you expect Poisson residuals for both nonlinear and multiple regression, and the calculations are done accordingly.
More built-in equations for nonlinear regression
•Pade(1,1) approximant equation, useful for interpolating when the standard curve plateaus at large concentrations (it is similar to a rectangular hyperbola).
•Family of growth equations. These are used for everything from the growth of cell number in culture to the growth of economies. Equations offered are exponential growth, exponential plateau, Gompertz, logistic, and beta (growth then decay).
•Several forms of the linear quadratic equation used to model cell death after exposure to radiation.
•Hinge function. It is the same as segmental linear regression, except that the two lines connect with a gentle curve instead of a hard angle.
•Fit straight lines to two data sets and find the intersection point and both slopes.
Asymmetrical (profile likelihood) confidence intervals
•Computing asymmetrical profile likelihood confidence intervals is 2-3 times faster.
•With difficult equations, Prism sometimes reported "???" rather than a value for one or both of the confidence limits. This can still happen, but much less often.
•When you choose to remove outliers in nonlinear regression, you can now ask Prism to create a results table with the "clean" data (without outliers).