ED 667 General Linear Models (Fall/Spring: 3)
Cross Listed with
Ph.D. students only; all others by instructor permission.
Addresses the construction, interpretation, and application of linear statistical
models. Specifically, lectures and computer exercises cover ordinary least
squares regression models; matrix algebra operations; parameter estimation
techniques; missing data options; power transformations; exploratory versus
confirmatory model building; linear-model diagnostics, sources of multicollinearity;
diagnostic residual analysis techniques; variance partitioning procedures;
dummy, effect, and orthogonal coding procedures; and an introduction to
structural equation modeling.
Larry Ludlow, Zhushan Mandy Li
Last Updated: 22-MAY-12