Given logistic regression is substantially more computationally efficient than Cox regression in both settings, we propose a two-step approach to GWAS in cohort and case-cohort studies.

The use of individualized logistic regression, in which a series of separate simple logistic regression analyses are performed as a replacement for polychotomous logistic regression, is studied. The ...

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 ...

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 ...

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

Laurence D. Robinson, Nicholas P. Jewell, Some Surprising Results about Covariate Adjustment in Logistic Regression Models, International Statistical Review / Revue Internationale de Statistique, Vol.

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

What are the advantages of logistic regression over decision trees? This question was originally answered on Quora by Claudia Perlich.