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 ...

Logistic regression is a statistical method used to examine the relationship between a binary outcome variable and one or more explanatory variables. It is a special case of a regression model that ...

Regression diagnostics will focus on evaluating the assumptions of regression, and recommendations when assumptions have not been met. Before you show up: Complete the simple pre-course assignment: Q1 ...

Estimation is considered for the class of conditional logistic regression models for clustered binary data proposed by Qu et al. (Communications in Statistics, Series A 16, 3447-3476, 1987) when ...

Influential observations are downweighted according to their leverage or residual in an example of correlated binary regression applied to 137 urinary incontinent elderly patients from 38 medical ...

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

A regression problem is one where the goal is to predict a single numeric value. For example, you might want to predict the annual income of a person based on their sex, age, state where they live and ...

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 ...

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.