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  "body": "\n## Summary\n\nThe probit model is a regression model for a binary response.\nThe probability of the outcome $1$ is the standard normal [[cumulative distribution function|0H8H7ZTHW2]] evaluated at a linear combination of regressors.\nIt can be read as a latent variable model: an unobserved normal quantity crosses a threshold exactly when the outcome is $1$.\nIt is a [[generalized linear model|K6C48KMRKC]] for Bernoulli responses with the probit link, and it is fitted by [[maximum likelihood|0FREPFKNS7]].\nIt is close to [[logistic regression|JN1PJ5AJJS]] in practice, and the choice between them is often a matter of convention in the field.\n\n## Explanations\n\n### L1 human\n\nThink of each person as having a hidden score, equal to a linear formula in their characteristics plus a normal random term.\nThe person says yes when the hidden score is above zero.\nThe probability of a yes is then the normal distribution function at the linear formula.\nFor example, if the linear formula equals $0$, the probability is $1/2$, and if it equals $1.96$, the probability is about $0.975$.\n",
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