Introduction to Advanced Statistics
The Advanced Statistics option includes procedures that offer more advanced modeling options than are available through the Statistics Base option.
- GLM Multivariate extends the general linear model provided by GLM Univariate to allow multiple dependent variables. A further extension, GLM Repeated Measures, allows repeated measurements of multiple dependent variables.
- Variance Components Analysis is a specific tool for decomposing the variability in a dependent variable into fixed and random components.
- Linear Mixed Models expands the general linear model so that the data are permitted to exhibit correlated and nonconstant variability. The mixed linear model, therefore, provides the flexibility of modeling not only the means of the data but the variances and covariances as well.
- Generalized Linear Models (GZLM) relaxes the assumption of normality for the error term and requires only that the dependent variable be linearly related to the predictors through a transformation, or link function. Generalized Estimating Equations (GEE) extends GZLM to allow repeated measurements.
- General Loglinear Analysis allows you to fit models for cross-classified count data, and Model Selection Loglinear Analysis can help you to choose between models.
- Logit Loglinear Analysis allows you to fit loglinear models for analyzing the relationship between a categorical dependent and one or more categorical predictors.
- Survival analysis is available through Life Tables for examining the distribution of time-to-event variables, possibly by levels of a factor variable; Kaplan-Meier Survival Analysis for examining the distribution of time-to-event variables, possibly 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.