Central to the idea of variance components models is the idea of fixed and random effects. Each effect in a variance components model must be classified as either a fixed or a random effect. Fixed ...

The main focus of this course will be on linear mixed models. That is, linear models with fixed effects and random effects. Some topics we’ll discuss are: When would I want to use a random effect? How ...

It is of great practical interest to simultaneously identify the important predictors that correspond to both the fixed and random effects components in a linear mixed-effects (LME) model. Typical ...

This technical note discusses fixed effects models. Though a unified example, the note shows how omitted variable bias can plague estimates in cross-section regressions and how focusing attention on ...

In many experimental situations, a response surface design is divided into several blocks to control an extraneous source of variation. The traditional approach in most response surface applications ...

Mixed Effect Models and Hierarchical Models Mixed Effect Models and Hierarchical Models Course Topics This course will discuss what mixed models are, why they are called "mixed" models, what is a ...

Defines the least-squares means for the fixed-effects general linear model. The report also discusses the use of least-squares means in lieu of class or subclass arithmetic means with unbalanced ...