IndianaUniversit y University Information Technology Services Regression Models for Binary Dependent Variables Using * Stata, SAS, R, LIMDEP, and SPSS Hun Myoung Park, Ph.D.
[email protected] © 2003-2010 Last modified on October 2010 University Information Technology Services Center for Statistical and Mathematical Computing Indiana University 410 North Park Avenue Bloomington, IN 47408 (812) 855-4724 (317) 278-4740 http://www.indiana.edu/~statmath * The citation of this document should read: “Park, Hun Myoung. 2009. Regression Models for Binary Dependent Variables Using Stata, SAS, R, LIMDEP, and SPSS. Working Paper. The University Information Technology Services (UITS) Center for Statistical and Mathematical Computing, Indiana University.” http://www.indiana.edu/~statmath/stat/all/cdvm/index.html © 2003-2010, The Trustees of Indiana University Regression Models for Binary Dependent Variables: 2 This document summarizes logit and probit regression models for binary dependent variables and illustrates how to estimate individual models using Stata 11, SAS 9.2, R 2.11, LIMDEP 9, and SPSS 18. 1. Introduction 2. Binary Logit Regression Model 3. Binary Probit Regression Model 4. Bivariate Probit Regression Models 5. Conclusion References 1. Introduction 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.