Logistic regression (logit model) is part of the family of generalized linear models (GLMs). Assuming statistical assumptions have been satisfied, a binary logistic regression model is appropriate when the outcome variable of interest is dichotomous (i.e., binary) and when the predictor variable(s) of interest is/are continuous (interval, ratio) or categorical (nominal, ordinal). Unlike ordinary least squares (OLS) estimation, which was covered previously in the context of simple linear regression and multiple linear regression, logistic regression coefficients are typically estimated using maximum likelihood (ML). There are also extensions of the logistic regression like multinomial and ordinal logistic regression, where these extensions are appropriate when the categorical outcome variable is nominal or ordinal with three or more levels/categories.
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