How do generalized linear models (GLMs) enhance data analysis?
Generalized linear models (GLMs) extend traditional linear regression by allowing for different types of outcome variables, such as binary, count, or multinomial outcomes. GLMs include logistic regression, Poisson regression, and negative binomial regression. These models are essential for analyzing data that do not meet the assumptions of normality and homoscedasticity.
