Generalized Linear Models

Generalized Linear Models (GLMs) are a flexible statistical framework used to analyze relationships between a set of input variables and an outcome that may not be a simple average. Unlike basic models that assume outcomes are normally distributed, GLMs can handle various types of data—such as counts, percentages, or binary results—by linking the predictors to the outcome through a specific function. This approach allows for accurate modeling of complex data patterns, making GLMs useful across many fields like healthcare, economics, and social sciences for understanding and predicting different kinds of outcomes.