How does a probit model work?

In practice, the probit model assumes that there exists an underlying latent variable that is a linear function of the predictors. This latent variable is then used to determine the probability of the observed binary outcome through the inverse standard normal distribution.

One of the key advantages of using the probit model over other binary outcome modeling techniques, such as logistic regression, is its ability to more accurately model the extreme tails of the probability distribution. This can be particularly useful when dealing with data that exhibits a high degree of skewness.

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Probit regression, a statistical methodology also referred to as the probit model, finds its application in modeling outcomes that are dichotomous or binary in nature. This model offers a unique approach to analyzing data where the dependent variable can take on only two possible values.

At the core of the probit model lies the concept of modeling the inverse standard normal distribution of the probability of an event occurring. This transformation allows for the linear relationship between the predictors and the probability of the outcome to be estimated.