What is the probit model used for?

The probit model is a statistical tool utilized in regression analysis where the outcome variable is dichotomous, meaning it can only assume two possible values. This model is particularly suited for scenarios where the researcher is interested in predicting the probability of an event occurring, such as whether an individual is married or not.

The name "probit" is a portmanteau, derived from the combination of "probability" and "unit." This reflects the model's underlying assumption that the probability of the event occurring is a function of the explanatory variables, with the unit referring to the discrete nature of the dependent variable.

The probit model is a type of generalized linear model (GLM), which allows for the modeling of the relationship between a response variable and one or more predictor variables when the response variable is not normally distributed. In the case of the probit model, the response variable follows a binomial distribution.

One of the key advantages of the probit model is its ability to handle categorical dependent variables. This makes it particularly useful in fields such as economics, sociology, and psychology, where researchers often need to model binary outcomes.

Another advantage of the probit model is its flexibility in incorporating multiple predictor variables. This allows researchers to account for the potentially complex relationships between the dependent and independent variables, providing a more nuanced understanding of the underlying processes.