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Generalized Linear Models and Extensions

A second year course in advanced statistical techniques that covers useful quantitative tools in health & policy research. Assuming a strong foundation in regression & the general linear model, this course focuses on data analysis that utilizes models for categorical, discrete or limited outcomes that are commonly seen in health & policy studies. Examples include health status, number of clinic visits, etc. In this course students will also learn the principles of likelihood-based inference, which will assist them in some of the more advanced statistics courses.

Course #
APSTA-GE 2044
Credits
2
Department
Applied Statistics, Social Science, and Humanities

Professors

Marc Scott

Co-Department Chair, Professor of Applied Statistics; Co-Director of PRIISM

marc.scott@nyu.edu