Fits models where the response is continuous. Techniques include regression, analysis of variance, analysis of covariance, mixed models, and analysis of designed experiments. See the Standard Least Squares Report and Options topic and Emphasis Options for Standard Least Squares.
Facilitates variable selection for standard least squares and ordinal logistic analyses (or nominal with a binary response). For continuous responses, cross validation, p-value, BIC, and AICc criteria are provided. Also provided are options for fitting all possible models and for model averaging. For logistic fits, p-value, BIC, and AICc criteria are provided. See the Stepwise Regression Models topic.
Generalized RegressionFits generalized linear models using regularized, also known as penalized, regression techniques. The regularization techniques include ridge regression, the lasso, the adaptive lasso, the elastic net, and the adaptive elastic net. The response distributions include the normal, binomial, Poisson, zero-inflated Poisson, negative binomial, zero-inflated negative binomial, and gamma. See the Generalized Regression Models topic and Distribution.
Mixed ModelFits models that involve multiple continuous Y variables. Techniques include multivariate analysis of variance, repeated measures, discriminant analysis, and canonical correlations. See the Multivariate Response Models topic.
For a continuous Y variable, constructs models for both the mean and the variance. You can specify different sets of effects for the two models. See the Loglinear Variance Models topic.
You can also launch this personality by selecting Analyze > Reliability and Survival > Fit Proportional Hazards. See Fit Parametric Survival in the Reliability and Survival Methods book.
You can also launch this personality by selecting Analyze > Reliability and Survival > Fit Parametric Survival. See Fit Parametric Survival in the Reliability and Survival Methods book.
Fits generalized linear models using various distribution and link functions. Techniques include logistic, Poisson, and exponential regression. See the Generalized Linear Models topic.
Partial Least SquaresFits models to one or more Y variables using latent factors. This permits models to be fit when explanatory variables (X variables) are highly correlated, or when there are more X variables than observations.
You can also launch a partial least squares analysis by selecting Analyze > Multivariate Methods > Partial Least Squares. See Partial Least Squares Models in the Multivariate Methods book.
Note: This personality allows only continuous responses. Response Screening for individual factors is also available by selecting Analyze > Screening > Response Screening. This platform supports categorical responses, and also provides equivalence tests and tests of practical significance. See Response Screening in the Predictive and Specialized Modeling book.