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dc.contributor.author Park, Hun Myoung
dc.date.accessioned 2015-03-25T19:49:00Z
dc.date.available 2015-03-25T19:49:00Z
dc.identifier.uri http://hdl.handle.net/2022/19740
dc.description.abstract A categorical variable here refers to a variable that is binary, ordinal, or nominal. Event count data are discrete (categorical) but often treated as continuous variables. When a dependent variable is categorical, the ordinary least squares (OLS) method can no longer produce the best linear unbiased estimator (BLUE); that is, OLS is biased and inefficient. Consequently, researchers have developed various regression models for categorical dependent variables. The nonlinearity of categorical dependent variable models makes it difficult to fit the models and interpret their results. en
dc.rights Copyright 2015 by the Trustees of Indiana University. This content is released under the Creative Commons Attribution 3.0 Unported license (http://creativecommons.org/licenses/by/3.0/). en
dc.rights.uri http://creativecommons.org/licenses/by/3.0/ en
dc.subject Regression Models, Binary Dependent Variables, Stata, SAS, R, LIMDEP, SPSS en
dc.title Regression Models for Binary Dependent Variables Using Stata, SAS, R, LIMDEP, and SPSS en
dc.altmetrics.display false en


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