Abstract
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://ssrn.com/abstract=360300 or http://dx.doi.org/10.2139/ssrn.360300) and (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 can be used for prospective, retrospective, or cross-sectional data while the probit, Tobit, and the 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://ssrn.com/abstract=360300 or http://dx.doi.org/10.2139/ssrn.360300; Logit models from economics and other fields, Cambridge University Press, Cambridge, England, 2003, pp. 149–158).
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Wilson, J.R., Lorenz, K.A. (2015). Short History of the Logistic Regression Model. In: Modeling Binary Correlated Responses using SAS, SPSS and R. ICSA Book Series in Statistics, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-23805-0_2
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DOI: https://doi.org/10.1007/978-3-319-23805-0_2
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