This section provides an overview of a likelihood-based approach to general linear mixed models. This approach simplifies and unifies many common statistical analyses, including those involving ...

This course will discuss the concept of random effects, why they are called random effects and how they are incorporated in the framework of mixed models. The primary focus of the course will be to ...

This course will discuss what mixed models are, why they are called "mixed" models, what is a "random factor", and why. The primary focus will be for the researcher to understand when he or she should ...

The PRIOR statement enables you to carry out a sampling-based Bayesian analysis in PROC MIXED. It currently operates only with variance component models. The analysis produces a SAS data set ...