Course Descriptions

The Course Descriptions catalog describes all undergraduate and graduate courses offered by Michigan State University. The searches below only return course versions Fall 2000 and forward. Please refer to the Archived Course Descriptions for versions prior to Fall 2000.

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Course Descriptions: Search Results

STT 864  Applied Statistical Methods II

Semester:
Spring of odd years
Credits:
Total Credits: 3   Lecture/Recitation/Discussion Hours: 3
Recommended Background:
STT 863
Description:
Generalized linear models, loglinear models, hierarchical models, repeated measures, discriminant analysis and classification, clustering, regression, classification trees, selected nonparametric methods.
Effective Dates:
FALL 2004 - FALL 2011


STT 864  Applied Statistical Methods II

Semester:
Spring of every year
Credits:
Total Credits: 3   Lecture/Recitation/Discussion Hours: 3
Recommended Background:
STT 863
Description:
Generalized linear models, loglinear models, hierarchical models, repeated measures, discriminant analysis and classification, clustering, regression, classification trees, selected nonparametric methods.
Effective Dates:
SPRING 2012 - SPRING 2012


STT 864  Statistical Methods II

Semester:
Spring of every year
Credits:
Total Credits: 3   Lecture/Recitation/Discussion Hours: 3
Recommended Background:
STT 863
Description:
Generalized linear models(GLMs). Deviance and residual analysis in GLMs. Analysis of two-way and three-way contingency tables. Logistic regression. Log-linear models. Multicategorical response models. Poisson regression. Zero-inflated Poisson regression. Negative-binomial models. Overdispersed models. Quasi-likelihood models. Introduction to generalized estimating equations. Introduction to longitudinal data. Introduction to semi-parametric regression.
Effective Dates:
SUMMER 2012 - SUMMER 2013


STT 864  Statistical Methods II

Semester:
Spring of every year
Credits:
Total Credits: 3   Lecture/Recitation/Discussion Hours: 3
Prerequisite:
STT 863
Description:
Generalized linear models(GLMs). Deviance and residual analysis in GLMs. Analysis of two-way and three-way contingency tables. Logistic regression. Log-linear models. Multicategorical response models. Poisson regression. Introduction to generalized estimating equations. Introduction to longitudinal data. Bayesian analysis using WinBUGS.
Effective Dates:
FALL 2013 - Open