This tutorial covers the case when Y is binary - that is, where it can take only two values, “0” and “1”, which represent outcomes such as pass/fail, win/lose, alive/dead or healthy/sick. It allows one to say that the presence of a predictor increases (or decreases) the probability of a given outcome by a specific percentage. Logistic regression allows us to estimate the probability of a categorical response based on one or more predictor variables ( X). Logistic regression (aka logit regression or logit model) was developed by statistician David Cox in 1958 and is a regression model where the response variable Y is categorical.
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