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

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

A class of conditional logistic regression models for clustered binary data is considered. This includes the polychotomous logistic model of Rosner (1984) as a special case. Properties such as the ...

Linear mixed models are increasingly used for the analysis of genome-wide association studies (GWAS) of binary phenotypes because they can efficiently and robustly account for population ...

This is a preview. Log in through your library . Abstract Logistic regression models are commonly used to study the association between a binary response variable and an exposure variable. Besides the ...

The following table details the results of a series of statistical models predicting various measures related to people’s attitudes toward electric vehicles from a set of explanatory variables, or ...