This article will cover the basic theory behind logistic regression, the types of logistic regression, when to use them and take you through a worked example.

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

The data doctor continues his exploration of Python-based machine learning techniques, explaining binary classification using logistic regression, which he likes for its simplicity.

Logistic regression enables you to investigate the relationship between a categorical outcome and a set of explanatory variables. The outcome, or response, can be dichotomous (yes, no) or ordinal (low ...

Example 29.1: Logistic Regression In an experiment comparing the effects of five different drugs, each drug is tested on a number of different subjects. The outcome of each experiment is the presence ...

Outcome-dependent sampling increases the efficiency of studies of rare outcomes, examples being case-control studies in epidemiology and choice-based sampling in econometrics. Two-phase or double ...

The Fisher information matrix for the estimated parameters in a multiple logistic regression can be approximated by the augmented Hessian matrix of the moment-generating function for the covariates.

I predict you'll find this logistic regression example with R to be helpful for gleaning useful information from common binary classification problems.