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A natural fit for count variables that follow the Poisson or negative binomial distribution is the log link. The log link exponential the linear predictors. It does not log transform the outcome variable. Here are two versions of the same basic model equation for count data: LN() = 0 + 1X.
Generalized linear models include a link function that relates the expected value of the response to the linear predictors in the model. A link function transforms the probabilities of the levels of a categorical response variable to a continuous scale that is unbounded.
For logistic regression, this is known as the logic link function. For the linear regression model, the link function is called the identity link function, because no transformation is needed to get from the linear regression parameters on the right-hand side of the equation to the normal distribution.
The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
A GLM consists of three components: A random component, A systematic component, and. A link function.
In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution.
The General Linear Model (GLM) is a useful framework for comparing how several variables affect different continuous variables. In its simplest form, GLM is described as: Data = Model + Error (Rutherford, 2001, p.3) GLM is the foundation for several statistical tests, including ANOVA, ANOVA and regression analysis.
The term general linear model (GLM) usually refers to conventional linear regression models for a continuous response variable given continuous and/or categorical predictors. It includes multiple linear regression, as well as ANOVA and ANOVA (with fixed effects only).

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