 IBM® SPSS® Statistics Standard Edition includes all the Base Edition capabilities plus features that support advanced modeling options, regression analysis and custom tables.

Take advantage of various regression procedures including logistic regression, quantile regression and more. You can leverage several advanced statistics procedures including GLM multivariate, variance components analysis, life tables, Bayesian statistics to name a few. Additionally, you can summarize your data and display analyses in production-ready tables with the Custom Tables module.

## Feature spotlights

### Regression

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

### Bayesian statistics

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.

### Multivariate general linear modeling (GLM)

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.

### Custom tables

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.

### Generalized linear models and generalized estimating equations

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.

### 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.

## Regression

• Binary logistic regression
• Logit response models

• Multinomial logistic regression
• Nonlinear regression

• Probit response analysis, two-stage least squares, weighted least squares, quantile regression

• 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