Generalized Linear Models: Theory and Applications

This lecture covers the theory and applications of Generalized Linear Models (GLMs). It starts with the basics of linear regression models and then delves into modified models and the logit transformation. The instructor explains the concept of GLMs, including the random component, linear predictor, and link function. Logistic regression and Poisson regression are discussed as examples. The lecture also explores the link function, multiple logistic regression, and interpreting model results. The importance of model evaluation through deviance and model comparison using likelihood ratio tests is highlighted. The lecture concludes with a summary of tests for coefficients in logistic regression models.