Probit Regression Vs Logistic Regression. In regression analysis logistic regression or logit regression is estimating the parameters of a logistic model a form of binary regression. Probit assumes a Gaussian normal distribution.
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A 1-unit difference in X will have a bigger impact on probability in the middle than near 0 or 1. Different disciplines tend to use one more frequently than the other although logistic regression is by far the most common. Probit although the logistic coefficients tend to be approximately 181 larger than probit coefficients.
This procedure includes a CLASS statement.
You could use the likelihood value of each model to decide for logit vs probit. Logistic regression can be interpreted as modelling log odds and the co-efficients in the logistic regression can be interpreted as odds ratio. Logistic is a short name for logistic unit while probit is a short name for probability unit. Probit Regression Z-scores Interpretation.
