In statistics, the ordered logit model (also ordered logistic regression or proportional odds model) is an ordinal regression model—that is, a regression model for ordinal dependent variables—first considered by Peter McCullagh. For example, if one question on a survey is to be answered by a choice among "poor", "fair", "good", "very good" and "excellent", and the purpose of the analysis is to see how well that response can be predicted by the responses to other questions, some of which may be quantitative, then ordered logistic regression may be used. It can be thought of as an extension of the logistic regression model that applies to dichotomous dependent variables, allowing for more than two (ordered) response categories.
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| - Ordered logit (en)
- Модель упорядоченного выбора (ru)
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| - In statistics, the ordered logit model (also ordered logistic regression or proportional odds model) is an ordinal regression model—that is, a regression model for ordinal dependent variables—first considered by Peter McCullagh. For example, if one question on a survey is to be answered by a choice among "poor", "fair", "good", "very good" and "excellent", and the purpose of the analysis is to see how well that response can be predicted by the responses to other questions, some of which may be quantitative, then ordered logistic regression may be used. It can be thought of as an extension of the logistic regression model that applies to dichotomous dependent variables, allowing for more than two (ordered) response categories. (en)
- Модель упорядоченного выбора (упорядоченная регрессия, англ. ordered choice) — применяемая в эконометрике модель с упорядоченной (с ранжированными значениями) дискретной зависимой переменной, в качестве которой могут выступать, например, оценки чего-либо по пятибалльной шкале, рейтинги компаний и т. д. В рамках данной модели предполагается, что количество значений зависимой переменной конечно. (ru)
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| - In statistics, the ordered logit model (also ordered logistic regression or proportional odds model) is an ordinal regression model—that is, a regression model for ordinal dependent variables—first considered by Peter McCullagh. For example, if one question on a survey is to be answered by a choice among "poor", "fair", "good", "very good" and "excellent", and the purpose of the analysis is to see how well that response can be predicted by the responses to other questions, some of which may be quantitative, then ordered logistic regression may be used. It can be thought of as an extension of the logistic regression model that applies to dichotomous dependent variables, allowing for more than two (ordered) response categories. (en)
- Модель упорядоченного выбора (упорядоченная регрессия, англ. ordered choice) — применяемая в эконометрике модель с упорядоченной (с ранжированными значениями) дискретной зависимой переменной, в качестве которой могут выступать, например, оценки чего-либо по пятибалльной шкале, рейтинги компаний и т. д. В рамках данной модели предполагается, что количество значений зависимой переменной конечно. (ru)
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