Logistic / Probit fit

A model that describes the relationship between a categorical response variable and one or more explanatory variables using a logit or probit function.

• 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.
• Parameter estimates
  Parameter estimates (also called coefficients) are associated with a one-unit change of the predictor, all other predictors being held constant.
• Odds ratio estimates
  Odds ratios are the increase or decrease in odds associated with a change of the predictor, all other predictors been held constant.
• Effect of model hypothesis test
  A likelihood ratio or Wald X² test formally tests the hypothesis of whether the model fits the data better than no model.
• Effect of term hypothesis test
  A likelihood ratio or Wald X² test formally tests the hypothesis of whether a term contributes to the model.