Short History of the Logistic Regression Model

The logistic regression model, as compared to the probit, Tobit, and complementary log-log models, is worth revisiting based upon the work of Cramer ( http://ssm.com/abstract=360300 or https://doi.org/10.2139/ssm.360300 : Logit models from economics and other fields. Cambridge University Press, Cambridge, England, 2003, pp. 149–158). The ability to model the odds has made the logistic regression model a popular method of statistical analysis. The logistic regression model is used for prospective, retrospective, or cross-sectional data, while other binary models such as the probit, Tobit, and complementary log-log models can only be used with prospective data because they model the probability of the event. This chapter provides a summary ( http://ssm.com/abstract=360300 or https://doi.org/10.2139/ssm.360300 : Logit models from economics and other fields. Cambridge University Press, Cambridge, England, 2003, pp. 149–158).