General Linear Model#
In the first statistics lecture, you were introduced to the General Linear Model (GLM), a powerful framework for understanding relationships between variables. Unlike traditional hypothesis testing, where the goal is simply to reject or accept a null hypothesis, GLMs offer a more flexible and nuanced approach. For example, how can we assess the impact of physical activity on diabetes risk while accounting for confounding factors like age or genetics? GLMs help us answer such complex questions.
In today’s seminar, we will build on this foundation by exploring the following key concepts:
Multiple Linear Regression: Modeling the relationship between one dependent variable and multiple predictors.
Correlation: Quantifying the strength and direction of relationships between continuous variables.
Partial Correlation: Isolating the relationship between two variables while controlling for others.
You will then apply these concepts to a dataset on diabetes and its risk factors. By the end of this seminar, you’ll have the tools to model and interpret complex relationships in real-world data.