Overview

Feature highlights

Boost your analytics with these features

Regression

Predict categorical outcomes and apply various nonlinear regression procedures.

Bayesian statistics

Use the Bayesian inference method of statistical inference to update the probability for a hypothesis as more information becomes available.

Multivariate general linear modeling (GLM)

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

Custom tables

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

Generalized linear models and generalized estimating equations

Expand the general linear model with the generalized linear models procedure so that the dependent variable is linearly related to the factors and covariates through a specified link function. Use the generalized estimating equations procedure to analyze repeated measurements or other correlated observations.

Survival analysis

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.

Standard Edition features

Regression

  • Binary logistic regression
  • Logit response models
  • Multinomial logistic regression
  • Nonlinear regression
  • Probit response analysis, two-stage least squares, weighted least squares, quantile regression

Advanced statistics

  • Cox regression
  • General linear modeling (GLM), general factorial, multivariate (MANOVA), repeated measures, variance components
  • Generalized linear models and generalized estimating equations, gamma regression, Poisson regression, negative binomial
  • GENLOG for loglinear and logit
  • Generalized linear mixed models (GLMM) (ordinal targets included)
  • Bayesian statistics
  • Hierarchical loglinear models
  • Kaplan-Meier
  • Linear mixed-level models (aka hierarchical linear models)
  • Survival
  • Variance component estimation

Custom tables

  • 35 descriptive statistics
  • Drag-and-drop interface
  • Inferential statistics
  • Nested tables
  • Place totals in any row, column, or layer
  • Post computed categories
  • Effective base for weighted sample results
  • Put multiple variables into the same table
  • Significance tests on multiple response variables
  • Significance test in custom tables main table
  • Significance values for column means and column proportion tests
  • Specialized multiple response set tables
  • False discovery correction method for multiple comparisons
  • Syntax converter
  • Table preview