Regression Models for Binary Dependent Variables Using Stata, SAS, R, LIMDEP, and SPSS

dc.altmetrics.display	false
dc.contributor.author	Park, Hun Myoung
dc.date.accessioned	2015-03-25T19:49:00Z
dc.date.available	2015-03-25T19:49:00Z
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.
dc.identifier.uri	https://hdl.handle.net/2022/19740
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/).
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/
dc.subject	Regression Models, Binary Dependent Variables, Stata, SAS, R, LIMDEP, SPSS
dc.title	Regression Models for Binary Dependent Variables Using Stata, SAS, R, LIMDEP, and SPSS

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