SPSS Regression enables you to predict categorical outcomes and apply various nonlinear regression procedures.

Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more information becomes available.

GLM multivariate modeling is used to model value of multiple dependent variables based on their relationships to categorical and scale predictors. GLM repeated measures allows repeated measurements of multiple dependent variables.

Summarize SPSS Statistics data, and display your analyses as presentation-quality, production-ready tables. With analytics capabilities and advanced features, you can build tables to interpret and learn from data.

The generalized linear models procedure expands the general linear model so that the dependent variable is linearly related to the factors and covariates through a specified link function. Using the generalized estimating equations procedure, you can analyze repeated measurements or other correlated observations.

Use life tables to examine the distribution of time-to-event variables, including by levels of a factor variable; Kaplan-Meier survival analysis for examining the distribution of time-to-event variables, including by levels of a factor variable or producing separate analyses by levels of a stratification variable; and Cox regression for modeling the time to a specified event, based upon the values of given covariates.