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STAT 473 Generalized Linear Models Units: 3.00
An introduction to advanced regression methods for binary, categorical, and count data. Major topics include maximum-likelihood method, binomial and Poisson regression, contingency tables, log linear models, and random effect models. The generalized linear models will be discussed both in theory and in applications to real data from a variety of sources. Given jointly with STAT 873.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Offering Faculty: Faculty of Arts and Science
Course Learning Outcomes:
- Compute numerical implementation of the scoring method for finding the maximum likelihood estimates in the cases of real and vector parameters.
- Compute numerical solution of equations and numerical maximization of expressions depending on a real parameter.
- Handle numerically various Poisson regression and binomial logistic regression models.
- Identify the best unbiased estimates for the unknown parameters of exponential families of distribution.