Fall 2019 - STAT 850 G100
Linear Models and Applications (4)
Class Number: 4607
Delivery Method: In Person
A modern approach to normal theory for general linear models including models with random effects and "messy" data. Topics include experimental units, blocking, theory of quadratic forms, linear contrasts, analysis of covariance, heterogeneous variances, factorial treatment structures, means comparisons, missing data, multi-unit designs, pseudoreplication, repeated measures mixed model formulation and estimation and inference.
1. Introduction; scope of linear models.
2. General theory; least squares and Gauss-Markov theorem; normal linear models; quadratic forms.
3. Anova models; design issues; block designs; fractional factorial designs.
4. Model selection; diagnostics; algorithms; selection criteria.
5. Multicollinearity; ridge regression; robust estimation; the bootstrap.
6. Mixed linear models; generalized linear models; nonparametric regression.
- Assignments 20%
- Presentation 20%
- Written Report 30%
- Final 30%
Above grading is subject to change.
Graduate Studies Notes:
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