IBM SPSS Advanced Statistics

IBM SPSS ADVANCED STATISTICS

Improve the accuracy of your analysis and the dependability of your conclusions with statistics designed for complex relationships. As a module of IBM SPSS Statistics, IBM SPSS Advanced Statistics provides a set of sophisticated uni-variate and multi-variate analytical techniques and models: generalized linear mixed models (GLMM), general linear models (GLM), mixed models procedures, generalized linear models (GENLIN) and generalized estimating equations (GEE) procedures. Gain deeper insights from data to help solve real-world problems in disciplines such as medical research, manufacturing, pharmaceuticals and market research.

Move beyond basic analysis

Improve analysis for multiple outcomes or outcomes over time, analyze data with hierarchical structure or estimate length of time until an event.

Build flexible models

The GLM procedure enables you to describe the relationship between a dependent variable and a set of independent variables. Models include linear regression, ANOVA, ANCOVA, MANOVA and MANCOVA.

Work better with nested-structure data

The linear mixed models procedure expands upon the models used in the GLM procedure so that you can analyze data that exhibit correlation and non-constant variability.

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More accurately analyze complex relationships

Improve analysis and conclusions with statistics designed to work with data describing complex relationships

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Features spotlight

Generalized estimating equations (GEE) procedures
Generalized linear mixed models (GLMM)
Linear mixed models/hierarchical linear models (HLM)
Generalized linear models (GENLIN)