Generalized linear models
A generalized linear model (GLM) is a generalization of the linear models by allowing the linear model to be related to the response variable via a link function and an error distribution other than a normal distribution. The unknown model parameters are estimated using maximum-likelihood estimation.
| Type | Description |
|---|---|
| Logit / Logistic | Fit a model to a binary response variable expressed by the logit link function (log
odds ratio) and binomial error distribution. Note: This model is very common as the parameter
estimates can be interpreted as the log-odds or back transformed into an odds ratio.
|
| Probit | Fit a model to a binary response variable expressed by the probit function and binomial error distribution. |
Available in Analyse-it Editions
Standard edition
Method Validation edition
Quality Control & Improvement edition
Ultimate edition
Standard edition
Method Validation edition
Quality Control & Improvement edition
Ultimate edition
