How do you specify a probit model?

In Probit regression, the regression function is modeled as a linear combination of the explanatory variables, with the resulting value passed through the Φ(⋅) function to obtain the predicted probability of the binary outcome. This predicted probability is then used to make predictions or inferences about the data.

Probit regression is a statistical modeling technique employed when the dependent variable in a regression analysis is binary in nature. This method relies on the cumulative standard normal distribution function, denoted as Φ(⋅), to model the relationship between the explanatory variables and the binary outcome.

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The use of Φ(⋅) in Probit regression allows for the interpretation of the model coefficients in terms of the probability of observing a particular binary outcome. Specifically, it enables the estimation of the likelihood that the dependent variable takes on a specific value, given the values of the explanatory variables.

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