The Probit Model is a type of regression used in statistics to analyze binary outcome variables, where the result can be either one of two categories, such as yes/no or success/failure. It estimates the probability that a certain event occurs based on one or more predictor variables. The model uses a cumulative normal distribution function to link the independent variables to the probability of the dependent variable being one of the two outcomes. In a Probit Model, the coefficients indicate how changes in the predictor variables affect the likelihood of the event occurring. This model is particularly useful in fields like economics, social sciences, and health research, where understanding binary outcomes is essential for decision-making and policy formulation.