Powerful modeling techniques for analyzing complex relationships

IBM® SPSS® Advanced Statistics provides univariate and multivariate modeling techniques to help users reach the most accurate conclusions when working with data describing complex relationships. These sophisticated analytical techniques are frequently applied to gain deeper insights from data used in disciplines such as medical research, manufacturing, pharmaceuticals and market research.

SPSS Advanced Statistics provides the following capabilities:

Linear Mixed Models estimated means

GLMM provides estimated marginal means to explain the impact of the predictor.

Linear Mixed Models estimated means

Generalized Linear Mixed Models model summary

A GLMM model summary shows how well the model fits the data.

Generalized Linear Mixed Models model summary

Generalized Linear Mixed Models visualization

GLMM provides a visual representation of your model to show you the strength of each predictor. In this example, the variable promo has the strongest impact on sales.

Generalized Linear Mixed Models visualization

Parameter estimates table

This is the traditional parameter estimates table for the overall model and individual effects. The coefficients show the relationship of each model parameter to Post-test score. For a continuous field, the coefficient is the expected change in test score for a unit increase in the value of the continuous field. For the categorical fields, the coefficient is the expected change in test score relative to the reference category of the categorical field.

Parameter estimates table

SPSS Advanced Statistics Screenshots

General linear models (GLM)

Generalized linear models (GENLIN)

Linear mixed models/hierarchical linear models (HLM)

Generalized estimating equations (GEE) procedures

Generalized linear mixed models (GLMM)

Survival analysis procedures

SPSS Advanced Statistics resources

Trial software: IBM SPSS Statistics Desktop

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Powerful modeling techniques for analyzing complex relationships

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