Classification: Logistic Regression

Logistic regression is a method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a binary variable (in which there are only two possible outcomes).

In logistic regression, the dependent variable is binary, i.e. it only contains data coded as 1 (TRUE, success, pregnant, etc.) or 0 (FALSE, failure, non-pregnant, etc.).

The goal of logistic regression is to find the best fitting model to describe the relationship between the outcome variable of interest (dependent variable = response or outcome variable) and a set of independent (predictor or explanatory) variables. Logistic regression generates the coefficients (and its standard errors and significance levels) of a formula to predict a logit transformation of the probability of presence of the characteristic of interest:

Logistic regression equation: