Probit and logit models are among the most used models of generalized linear models in the case of binary or dichotomous dependent variables. The difference between Logistic and Probit models lies in the assumption about the distribution of the errors. The logit and probit are both sigmoid functions with a domain between 0 and 1. In the probit model, the inverse standard normal distribution of the probability is modeled as a linear combination of the predictors. In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables and the logit is the quantile function of the logistic distribution.
