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Search Results
| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
EPPS 7344 (Overall) | |||||
A | |||||
EPPS 7344 Michael Tiefelsdorf | |||||
A | |||||
Search Results
| Name | Grades | Rating | |||
|---|---|---|---|---|---|
Not teaching in Spring 2026 | |||||
EPPS 7344 (Overall) | |||||
A | |||||
EPPS 7344 Michael Tiefelsdorf | |||||
A | |||||
Categorical and Limited Dependent Variables
EPPS 7344
School of Economic, Political and Policy Sciences
This course examines several types of advanced regression models that are frequently used in policy analysis and social science research. The key similarity of these models is that they involve dependent variables that violate one or more of the assumptions of the Ordinary Least Squares (OLS) regression model. The main models examined in the course are binary logit and probit, multinomial logit, ordinal probit, tobit, and the family of Poisson regression models. All these models are estimated using maximum likelihood estimation (MLE). The Heckman correction for selection is also addressed. Recommended: EPPS 6316 or the equivalent. 3 credit hours.
Offering Frequency: Every two years
Grades: 9
Median GPA: A
Mean GPA: 3.594
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Categorical and Limited Dependent Variables
EPPS 7344
School of Economic, Political and Policy Sciences
This course examines several types of advanced regression models that are frequently used in policy analysis and social science research. The key similarity of these models is that they involve dependent variables that violate one or more of the assumptions of the Ordinary Least Squares (OLS) regression model. The main models examined in the course are binary logit and probit, multinomial logit, ordinal probit, tobit, and the family of Poisson regression models. All these models are estimated using maximum likelihood estimation (MLE). The Heckman correction for selection is also addressed. Recommended: EPPS 6316 or the equivalent. 3 credit hours.
Offering Frequency: Every two years
Grades: 9
Median GPA: A
Mean GPA: 3.594
Click a checkbox to add something to compare.