S632: Applied Linear Models II
In regression model: residual analysis, transformation, principal component regression. Generalized linear models: logit, probit, ordinal, multinomial and poisson regression. Linear mixed models. Design of experiments: factorial design, block design.
Semester(s) Offered: Spring
Class time: Monday, Wednesday, Friday : 9:05-9:55
Instructor: Arturo Valdivia - firstname.lastname@example.org
Other Contact(s): Arturo Valdivia - email@example.com
Sequence: Stat 631: Applied Linear Models I
Prerequisites: Stat 631
Algebra Required?: For proofs and homework
Calculus Required?: For derivations and homework
Day(s) per week offered: Three lectures a week; no computer labs
Recommended follow-up classes: Most other graduate level statistics courses.
Statistical Orientation: Most units on campus including Statistics, Social Sciences, Biological Sciences, Informatics, Education.mpus including Statistics, Social Sciences, Biological Sciences, Informatics, Education.
Books used: Lecture notes; Demidenko. 2013. Mixed models: theory and applications with R, 2nd Ed. D. Montegomery. 2008. Design and Analysis of Experiments, 7th Edition; A. Agresti. 2015 Foundations of linear and generalized linear models.
Applied/Theoretical: in the middle of a theoretical/applied
Software Used: R
How the software is used: Computation, programming and data analysis.
Problem Sets: Yes, proofs and derivations.
Data Analysis: Yes.
Keywords: Generalized linear models, linear mixed models, design of experiments