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.

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

Course Topics"Logistic and Poisson Regression," Wednesday, November 5: The fourth LISA mini course focuses on appropriate model building for categorical response data, specifically binary and count ...

The authors argue that for the cross-sectional multiattribute approach to choice modeling, the multinomial logit is theoretically and empirically superior to the more commonly used regression approach ...

Regression can be used on categorical responses to estimate probabilities and to classify.

A logistic regression for these data is a generalized linear model with response equal to the binomial proportion r/n. The probability distribution is binomial, and the link function is logit.

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 ...

Paul D. Allison, Nicholas A. Christakis, Logit Models for Sets of Ranked Items, Sociological Methodology, Vol. 24 (1994), pp. 199-228 ...