Data Modeling with Regression

Catalog description

This course provides a comprehensive introduction to generalized linear models (GLMs), allows modeling of various types of response variables, including continuous, binary, count, and categorical outcomes. Topics include estimation using maximum likelihood and least square methods, model diagnostics, model selection, and computation of sample size and power. Special emphasis is placed on linear, logistic, Poisson, and extensions such as overdispersion and zero-inflation regression models. Real-world applications in public health and biomedical sciences will be emphasized through hypothesis testing using statistical software like R and SAS.