Probit model

Summary

The probit model is a regression model for a binary response. The probability of the outcome $1$ is the standard normal cumulative distribution function evaluated at a linear combination of regressors. It can be read as a latent variable model: an unobserved normal quantity crosses a threshold exactly when the outcome is $1$. It is a generalized linear model for Bernoulli responses with the probit link, and it is fitted by maximum likelihood. It is close to logistic regression in practice, and the choice between them is often a matter of convention in the field.