What is GLM used for?

In the realm of binary data, GLMs can be employed to model outcomes that can be classified into two distinct categories, such as success/failure or yes/no. This type of modeling is essential in fields like medicine, marketing, and social sciences.

Generalized Linear Models (GLMs) have emerged as a powerful tool in statistical analysis, particularly when dealing with binary or count data. Their flexibility and adaptability make them ideal for a wide range of applications.

In addition to their versatility, GLMs are also known for their robustness. They can handle outliers and violations of distributional assumptions relatively well, making them a reliable choice for a wide range of data types.

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Count data, on the other hand, refers to observations that can take on non-negative integer values. Examples include the number of occurrences of an event, the number of items in a collection, or the count of a specific type of cell in a sample. GLMs are equally adept at handling such data types.