Logistic regression models are members of the generalized linear models family. Like the more common member, linear regression, they aim to estimate the relationship between independent (explanatory) variables and one dependent (outcome) variable. The main difference is that, with logistic regression, the outcome variable is binary, meaning it can only present two different states such as dead or alive, yes or no, present or absent. This simple characteristic creates some specific differences in the interpretation of the results and opens many possibilities. Outcome variables can be transformed to become binary in certain situations. For example, if a nonlinear behavior is suspected in regard to a continuous variable, then the transformation into a two-categories variable can offer a better solution. Also, Likert-type scale variables (i.e., completely ...

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