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