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Master CDA and GLM applications
·
Module 6
Foundations of Generalized Linear Models (GLMs)
1
Beyond OLS: Handling Non-Normal Responses
Explain why ordinary linear regression is insufficient for non-normal response variables like binary outcomes or counts.
2
Understanding the Three Components of a GLM
Identify the three core components of a GLM: random component, systematic component, and link function.
3
The Exponential Family and GLMs
Explain the concept of the exponential family of distributions and its role in unifying the GLM framework.
4
Canonical Links for GLMs
Define a canonical link function and identify the canonical links for the Normal, Binomial, and Poisson distributions.
5
Canonical vs. Non-Canonical Link Functions: Trade-offs in Selection
Distinguish between canonical and non-canonical link functions (e.g., logit vs. probit) and explain the trade-offs in their selection.
Previous module
Controlling for Confounding with Stratified Analysis
Next module
Inference and Model Assessment for GLMs