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.

However, coding multi-class logistic regression from scratch has least four advantages over using a library. Your code can be small and efficient, you can avoid licensing and copyright issues, you ...

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

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 ...

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

Informal and nontechnical, this book both explains the theory behind logistic regression and looks at all the practical details involved in its implementation using the SAS System. Several social ...

Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual ...

The modelling of correlated binary outcomes, in such a way that the marginal response probabilities are still logistic, is considered. Different association measures for the dependence between ...