The following SAS/STAT software examples are grouped according to the type of statistical analysis that is being performed. These examples are not included in the SAS/STAT documentation and are available only on the Web.

- Bayesian Zero-Inflated Poisson Regression
- Bayesian Exponential Mixture Model
- Bayesian Linear Regression with Standardized Covariates
- Bayesian Hierarchical Modeling for Meta-Analysis
- Bayesian Hierarchical Poisson Regression Model for Overdispersed Count Data Using SAS/STAT 9.2
- Bayesian Hierarchical Poisson Regression Model for Overdispersed Count Data Using SAS/STAT 9.3
- Bayesian Binomial Model with Power Prior Using the MCMC Procedure
- Bayesian Multivariate Prior for Multiple Linear Regression Using SAS/STAT 9.2
- Bayesian Multivariate Prior for Multiple Linear Regression Using SAS/STAT 9.3
- Bayesian Multinomial Model for Ordinal Data Using SAS/STAT 9.2
- Bayesian Multinomial Model for Ordinal Data Using SAS/STAT 9.3
- Bayesian Quantile Regression
- Bayesian LASSO
- Stochastic Search Variable Selection with PROC MCMC
- Bayesian IRT Models: Unidimensional Binary Models
- Bayesian Unidimensional IRT Models: Graded Response Model
- Bayesian Autoregressive and Time-Varying Coefficients Time Series Models

- Fitting Zero-Inflated Count Data Models by Using PROC GENMOD
- High-Performance Variable Selection for Generalized Linear Models: PROC HPGENSELECT
- Fitting Tweedie's Compound Poisson-Gamma Mixture Model by Using PROC HPGENSELECT

- Using Bootstrap Replicate Weights with SAS/STAT Survey Procedures
- Estimating Geometric Means Using Data from a Complex Survey Sampling Design
- Estimating the Variance of a Variable in a Finite Population
- Estimating the Standard Deviation of a Variable in a Finite Population
- Poststratification with PROC SURVEYMEANS
- Poisson Regressions for Complex Surveys