Analytical closed-form solution for binary logit regression by categorical predictors

Stan Lipovetsky

Journal of Applied Statistics, 2015, vol. 42, issue 1, 37-49

Abstract: In contrast to the common belief that the logit model has no analytical presentation, it is possible to find such a solution in the case of categorical predictors. This paper shows that a binary logistic regression by categorical explanatory variables can be constructed in a closed-form solution. No special software and no iterative procedures of nonlinear estimation are needed to obtain a model with all its parameters and characteristics, including coefficients of regression, their standard errors and t -statistics, as well as the residual and null deviances. The derivation is performed for logistic models with one binary or categorical predictor, and several binary or categorical predictors. The analytical formulae can be used for arithmetical calculation of all the parameters of the logit regression. The explicit expressions for the characteristics of logit regression are convenient for the analysis and interpretation of the results of logistic modeling.

Date: 2015
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Citations: View citations in EconPapers (3)

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DOI: 10.1080/02664763.2014.932760

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