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 regression employs a logistic function with a sigmoid (S-shaped) curve to map linear combinations of predictions and their probabilities. Sigmoid functions map any real value into ...

Linear and logistic regression models are essential tools for quantifying the relationship between outcomes and exposures. Understanding the mathematics behind these models and being able to apply ...

The LOGISTIC and PROBIT procedures can perform logistic and ordinal logistic regression. See Chapter 5, "Introduction to Categorical Data Analysis Procedures," Chapter 39, "The LOGISTIC Procedure," ...

In recent columns we showed how linear regression can be used to predict a continuous dependent variable given other independent variables 1,2. When the dependent variable is categorical, a common ...

A new study investigated how logistic regression model training affects performance, and which features are best to include when examining datasets from individuals suffering from COVID-19.

If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Logistic Regression Using the SAS System: Theory and Application is for ...

Linear and non-linear credit scoring by combining logistic regression and support vector machines Tony Van Gestel, Bart Baesens, Peter Van Dijcke, Johan A. K. Suykens and Joao Garcia ... approach that ...