Generalized Linear Models with Applications

CT Score

1500

#0 of 0 in SPH BS

Difficulty

—/10

Workload

—h/wk

Median grade

—

from grade distribution

Would take again

—

n = 0 verified

Grade distribution

No distribution published for this course.

By professor: same course, different game

PROFESSORSCOREGRADEHRS/WKTERMS

Professor ratings →

Sections

No section data for this term yet.

In the tree

(SPH BS 805) or consent of instructor - This course introduces statistical models for the analysis of quantitative and qualitative data, of the types usually encountered in health science research. The statistical models discussed include: Logistic regression for binary and binomial data, Nominal and Ordinal Multinomial logistic regression for multinomial data, Poisson regression for count data, and Gamma regression for data with constant coefficient of variation. All of these models are covered as special cases of the Generalized Linear Statistical Model, which provides an overarching statistical framework for these models. We will also introduce Generalized Estimating Equations (GEE) as an extension to the generalized models to the case of repeated measures data. The course emphasizes practical applications, making extensive use of SAS for data analysis→SPH BS 853→unlocks 0 courses

No mapped courses require this one yet. See on the map →

Study groups

0 active

🔒 Verified-only writes. You post as “sophomore, lecture section”: real enough to trust, anonymous enough to be honest.