Logistic regression is often used instead of Cox regression to analyse genome-wide association studies (GWAS) of single-nucleotide polymorphisms (SNPs) and disease outcomes with cohort and case-cohort ...

The first step in conducting a regression-based study is to specify a model. In real applications, this is usually the most challenging step - deciding which variables “belong” in the model and which ...

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on this powerful machine learning technique used to predict a single numeric value. A regression problem is one ...

Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.

A study conducted by experts from the University of the Philippines-Diliman showed that logistic regression is a reliable model for predicting the performance of licensure examination takers. Released ...

Dr. James McCaffrey of Microsoft Research explains how to evaluate, save and use a trained regression model, used to predict a single numeric value such as the annual revenue of a new restaurant based ...

Median regression models become an attractive alternative to mean regression models when employing flexible families of distributions for the errors. Classical approaches are typically algorithmic ...

A hybrid modeling framework to optimize Chinese hamster ovary cell cultures for monoclonal antibody (mAb) production reduces the number of modeling parameters needed while returning results that ...