Logistic Regression: A Concise Technical Overview

A popular statistical technique to predict binomial outcomes (y 0 or 1) is Logistic Regression. Logistic regression predicts categorical outcomes (binomial / multinomial values of y), whereas linear Regression is good for predicting continuous-valued outcomes (such as weight of a person in kg, the amount of rainfall in cm). The predictions of Logistic Regression (henceforth, LogR in this article) are in the form of probabilities of an event occurring, ie the probability of y 1, given certain values of input variables x. As shown in Figure1, the logit function on the right- with a range of - to, is the inverse of the logistic function shown on the left- with a range of 0 to 1. Estimating the values of B0,B1,..,Bk involves the concepts of probability, odds and log odds. The example dataset here is sourced from the UCLA website. The task is to predict which students graduated with honours or not (y 1 or 0), for 200 students with fields female, read, write, math, hon, femalexmath .