Instructor: Stavroula Chrysanthopoulou
Textbook: Dobson, A.J. and Barnett, A.G. (2018). An Introduction to Generalized Linear Models (4th Edition). Chapman and Hall/CRC. ISBN-13 978-1-584-88950-2.
Description: This course was designed for graduate and advanced undergraduate students who want to develop a practical hands-on toolkit for analyzing data and/or understand the theoretical underpinnings of regression. It provided a foundation for statistical theory and implementation of generalized linear models (GLMs) used to fit data and answer important questions in the area of clinical and population health research. Moreover, the course focused on a survey of applied GLMs for outcomes common in public health data, including continuous, binary, count, and survival data, where topics ranged from exploratory analysis, estimation, model building, diagnostics, and prediction. Emphasis was placed on understanding theoretical implications and on the implementation of GLMs to solve real world problems. Extensive use of computer programming was required for implementing GLM methods to analyze data from a range of medical and pharmaceutical applications, as well as public health and the social sciences.
Generalized linear models extend the linear regression framework to handle a wide range of response variables and distributions. They offer a flexible and powerful approach for modeling complex relationships, accommodating various data types, and making meaningful inferences from the data.
Assignments:
Fall ‘21