21. If just the dependent variable is ordinal, ordered probit or ordered logit can be used. 22. Closely related to the logit function ( and logit model ) are the probit function and probit model. 23. Closely related to the logit function ( and logit model ) are the probit function and probit model. 24. For ordinal variables with more than two values, there are the ordered logit and ordered probit models. 25. For this reason, models such as the logit model or the probit model are more commonly used. 26. If the dependent variable is a dummy variable, then logistic regression or probit regression is commonly employed. 27. The distributions were asymmetric to accommodate candidates with low expected vote shares ( calculated using a Probit transformation ). 28. In current statistical practice, probit and logit regression models are often handled as cases of the generalized linear model. 29. The inverse Mills ratio must be generated from the estimation of a probit model, a logit cannot be used. 30. If the dependent variable is discrete, some of those superior methods are logistic regression, multinomial logit and probit models.
