GLMmodellek

GLMmodellek refers to generalized linear models, a flexible statistical framework that extends linear regression. It allows for response variables that have error distribution models other than a normal distribution, and for the linear predictor to be related to the response variable via a link function. The three main components of a GLM are the random component, the systematic component, and the link function. The random component specifies the probability distribution of the response variable, such as Bernoulli for binary outcomes, Poisson for count data, or Gamma for positive, continuous data. The systematic component is a linear combination of predictor variables, similar to ordinary linear regression. The link function connects the expected value of the response variable to the linear predictor. Common link functions include the identity link (for normal distributions), the logit link (for Bernoulli distributions), and the log link (for Poisson distributions). GLMs are widely used in various fields, including biology, economics, and social sciences, to model a broad range of data types where traditional linear models are insufficient. Examples include modeling disease prevalence, predicting customer purchase counts, or analyzing survival times.