What are GLM ( Generalized Linear Models)

GLMs can handle a wider range of data distributions.

Explanation:

Generalized Linear Models (GLMs) are an extension of traditional linear regression models that can handle response variables with distributions other than the normal distribution. For example:

Binary outcomes (using logistic regression).

Count data (using Poisson regression).

Proportions (using binomial regression).

Key Advantages of GLMs:

Wider range of distributions: GLMs use a link function to relate the mean of the response variable to the linear predictor, allowing flexibility in modeling different types of data distributions.

Relaxation of linearity assumptions: GLMs allow for non-linear relationships between predictors and the response variable through the link function.

Misconceptions about the other options:

"Simpler to interpret than regression models": Not always true; the interpretation of coefficients in GLMs depends on the link function, which can make them less intuitive than traditional regression.

"Less computationally intensive": GLMs can be more computationally intensive due to their iterative fitting procedures (e.g., maximum likelihood estimation).

"Do not require the assumption of linearity": This is somewhat true in the sense that the relationship between the predictors and the response variable is modeled through a link function, but the linearity assumption still applies to the predictors in the linear predictor.