Statistics.com online course: Generalized Linear Models
Profs. Joseph Hilbe and James Hardin will be giving their online course
“Generalized Linear Models” (GLM) May 6 – June 3 at statistics.com. GLM
extends ordinary least squares (OLS) regression to incorporate responses
other than normal. This course will explain the theory of generalized
linear models (GLM), outline the algorithms used for GLM estimation, and
explain how to determine which algorithm to use for a given data
analysis. For continuous response variables, the log normal, gamma,
log-gamma (survival analysis), and inverse Gaussian cases are
covered. Binomial (logit, probit, and others) as well as count models
(poisson, negative binomial, geometric) are also covered.
Profs. Hilbe and Hardin are the co-authors of “Generalized Linear Models
and Extensions,” as well as “Generalized Estimating Equations”. Prof. Hilbe
(Arizona State – Sociology and Statistics) is currently software reviews
editor for “The American Statistician.” Prof. Hardin (Univ. of S. Carolina
– Epidemiology & Biostatistics) lectures widely in the area of GLM and GEE
(generalized estimating equations) and, with Dr. Hilbe, has been
instrumental in developing software routines for these methods for major
statistical software companies.
The course requires about 10 hours a week and offers interaction with the
instructor via a private discussion board, reading assignments and
exercises. There are no set hours when you must be available.
Register at:
http://www.statistics.com/content/courses/glm/index.html
Peter Bruce
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