To construct a generalized linear model, you must select response and explanatory variables for your data. You then must choose an appropriate link function and probability distribution for your response. Explanatory variables can be any combination of continuous variables, classification variables, and interactions. Some common examples of generalized linear models are listed in Examples of Generalized Linear Models.
The platform fits a generalized linear model to the data by maximum likelihood estimation of the parameter vector. In general, there is no closedform solution for the maximum likelihood estimates of the parameters. Therefore, the platform estimates the parameters of the model numerically through an iterative fitting process using a technique pioneered by Nelder and Wedderburn (1972). The overdispersion parameter φ is estimated by dividing the Pearson goodnessoffit statistic by its degrees of freedom. Covariances, standard errors, and confidence limits are computed for the estimated parameters based on the asymptotic normality of maximum likelihood estimators.
A number of link functions and probability distributions are available in the Generalized Linear Model personality of the Fit Model platform. Builtin Link Functions lists the builtin link functions.
g(μ) =


Variance Functions for Response Distributions lists the variance functions associated with the available distributions for the response variable.
V(μ) = 1

