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Generalized Linear Models
This lecture covers Generalized Linear Models (GLMs), focusing on the log likelihood function, deviance, and examples. It delves into the likelihood for GLMs, link functions for binary outcomes, and retrospective vs. prospective sampling. The lecture also discusses Poisson regression and over-dispersion, showcasing examples and how to check for over-dispersion. Additionally, it explores Quasi-Poisson regression and Negative Binomial regression as alternatives to Poisson regression when data are over-dispersed.
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