How do you calculate probit value?

The core advantage of Probit analysis lies in its ability to handle nonlinear relationships between the dependent variable and the explanatory variables. It assumes that the latent variable underlying the binary outcome follows a normal distribution, allowing for more flexibility in capturing complex relationships.

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Probit analysis is a statistical methodology that utilizes the Probit function, denoted as Probit(p) = φ-1(p), where φ represents the cumulative distribution function of the standard normal distribution.

This relationship implies that the inverse of the Probit function applied to the cumulative normal distribution equals the original value, i.e., φ(Probit(p)) = p. Conversely, the Probit function applied to the cumulative normal distribution of a variable z returns z itself, stated as Probit(φ(z)) = z.

Probit analysis finds its application in modeling scenarios where the dependent variable is dichotomous or binary in nature. Dichotomous variables take on only two possible values, such as success/failure or yes/no, making them suitable for analyzing binary outcomes.