ABSTRACT
Linear regression is used to approximate the relationship between a continuous response variable and a set of predictor variables. This chapter explores the use of logistic regression for binary response variables. Logistic regression provides an alternative to linear regression for binary classification problems. However, similar to linear regression, logistic regression suffers from the many assumptions involved in the algorithm. Although multinomial extensions of logistic regression exist, the assumptions made only increase and, often, the stability of the coefficient estimates decrease. Bear in mind that the coefficient estimates from logistic regression characterize the relationship between the predictor and response variable on a log-odds scale. Many aspects of the logistic regression output are similar to those discussed for linear regression.
