Statistics - Advanced Linear/Nonlinear Models - Fixed Nonlinear Regression

Ribbon bar. Select the Statistics tab. In the Advanced/Multivariate group, click Advanced Models and on the menu, select Fixed Nonlinear Regression to display the Fixed Nonlinear Regression Startup Panel.

Classic menus. On the Statistics - Advanced Linear/Nonlinear Models submenu, select Fixed Nonlinear Regression to display the Fixed Nonlinear Regression Startup Panel.

The Fixed Nonlinear Regression module is used to specify nonlinear transformations of variables. These transformed variables are then used in a regression analysis. The available transformations are: X to the second, third, fourth, or fifth power, the square root of X, natural log of X, log base 10 of X, Euler (e) = 2.71… to the power of X, 10 to the power of X, and the inverse of X.

The Fixed Nonlinear Regression module includes the same options as the Multiple Regression module for specifying multiple regression models, and/or to request stepwise forward or backward selection of predictors. Like the Multiple Regression module, the Fixed Nonlinear Regression facilities will calculate a comprehensive set of statistics and extended diagnostics including the complete regression table (with standard errors for B, Beta and intercept, R-square and adjusted R-square for intercept and non-intercept models, and ANOVA table for the regression), part and partial correlation matrices, correlations and covariances for regression weights, the sweep matrix (matrix inverse), the Durbin-Watson d statistic, Mahalanobis and Cook's distances, deleted residuals, confidence intervals for predicted values, and many others. The extensive residual and outlier analysis options include a large selection of plots, including a variety of scatterplots, histograms, normal and half-normal probability plots, detrended plots, partial correlation plots, different casewise residual and outlier plots and diagrams, and others.

See also the General Regression Models (GRM) and General Linear Models (GLM) modules for methods for fitting response surface and polynomial regression models; nonlinear regression models can also be fit in the General Nonlinear Estimation and Generalized Linear/Nonlinear Models (GLZ) modules.