When the dependent variable is categorical, a common approach is to use logistic regression, a method that takes its name from the type of curve it uses to fit data. Categorical variables are ...

As the coronavirus disease 2019 (COVID-19) pandemic has spread across the world, vast amounts of bioinformatics data have been created and analyzed, and logistic regression models have been key to ...

"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 ...

In matched case-control studies, conditional logistic regression is used to investigate the relationship between an outcome of being a case or a control and a set of prognostic factors. When each ...

Logistic regression is often considered the most fundamental. Other techniques include neural network binary classification, AdaBoost classification, Naive Bayes binary classification (for categorical ...

If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression ... analysis and GEE estimation for longitudinal binary data. Only basic ...

Logistic regression ... disadvantages with particular data sets that could force the investigator to discard some methods and prefer another method, according to the data at hand. A disadvantage of ...