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:
General linear models (GLM) and mixed models procedures.
Generalized linear models (GENLIN) including widely used statistical models, such as linear regression for normally distributed responses, logistic models for binary data and loglinear models for count data.
Linear mixed models, also known as hierarchical linear models (HLM), which expands the general linear models used in the GLM procedure so that you can analyze data that exhibit correlation and non-constant variability.
Generalized estimating equations (GEE) procedures that extend generalized linear models to accommodate correlated longitudinal data and clustered data.
Generalized linear mixed models (GLMM) for use with hierarchical data and a wide range of outcomes, including ordinal values.
Survival analysis procedures for examining lifetime or duration data.
General linear models (GLM)
Describe the relationship between a dependent variable and a set of independent variables. Models include linear regression, analysis of variance (ANOVA), analysis of covariance (ANCOVA), multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA).
Use flexible design and contrast options to estimate means and variances and to test and predict means.
Mix and match categorical and continuous predictors to build models, choosing from many model-building possibilities.
Use linear mixed models for greater accuracy when predicting nonlinear outcomes, such as what a customer is likely to buy, by taking into account hierarchical and nested data structures.
Formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance and randomized complete blocks design.
Generalized linear models (GENLIN)
Provide a unifying framework that includes classical linear models with normally distributed dependent variables, logistic and probit models for binary data, and loglinear models for count data, as well as various other nonstandard regression-type models.
Apply many useful general statistical models including ordinal regression, Tweedie regression, Poisson regression, Gamma regression and negative binomial regression.
Linear mixed models/hierarchical linear models (HLM)
Model means, variances and covariances in data that display correlation and non-constant variability, such as students nested within classrooms or consumers nested within families.
Formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance, and randomized complete blocks design.
Select from 11 non-spatial covariance types, including first-order ante-dependence, heterogeneous, and first-order autoregressive.
Get more accurate results when working with repeated measures data, including situations in which there are different numbers of repeated measurements, different intervals for different cases, or both.
Generalized estimating equations (GEE) procedures
Extend generalized linear models to accommodate correlated longitudinal data and clustered data.
Model correlations within subjects.
Generalized linear mixed models (GLMM)
Access, manage and analyze virtually any kind of data set including survey data, corporate databases or data downloaded from the web.
Run the GLMM procedure with ordinal values to build more accurate models when predicting nonlinear outcomes such as whether a customer’s satisfaction level will fall under the low, medium or high category.
Survival analysis procedures
Choose from a flexible and comprehensive set of techniques for understanding terminal events such as part failure, death or survival rates.
Use Kaplan-Meier estimations to gauge the length of time to an event.
Select Cox regression to perform proportional hazard regression with time-to-response or duration response as the dependent variable.
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