Indiana University Bloomington

Statistics S632
Applied Linear Models Il

Contact: Arturo Valdivia (artvaldi@indiana.edu) 2015-10-14
Offered: Spring, Every Year
Class Time: 9:05:9:55
Class Days: M, W, F
Capacity: 50
Sequence: Stat 631: Applied Linear Models I
Pre-Requisites: Stat 631
Algebra Required: For proofs and homework.
Calculus Required: For derivations and homework.
Instructor: Arturo Valdivia
Days Per Week Offered: Three lectures a week; no computer labs
Website: http://oncourse.iu.edu
Recommended follow-up classes: Most other graduate level statistics courses.
Syllabus: Download Syllabus
Keywords: Generalized linear models, linear mixed models, design of experiments
Description: 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.
Books: 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.
Statistical Orientation: Most units on campus including Statistics, Social Sciences, Biological Sciences, Informatics, Education.mpus including Statistics, Social Sciences, Biological Sciences, Informatics, Education.
Applied/Theoretical: In the middle of theoretical ? applied.
Software: R
How Software is Used: Computation, programming and data analysis.
Problem Sets: Yes, proofs and derivations.
Data Analysis: Yes.
Presentation: Yes.
Exams: Yes.
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