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 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 ...

Binary-response regression models in which the link function is a family defined by one or more unknown shape parameters are considered. Detailed attention is given to the two single-parameter ...

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to ...

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

Dr. James McCaffrey of Microsoft Research uses code samples, a full C# program and screenshots to detail the ins and outs of kernal logistic regression, a machine learning technique that extends ...

What are the advantages of logistic regression over decision trees? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand ...

Multicenter Phase I/II Study of Cetuximab With Paclitaxel and Carboplatin in Untreated Patients With Stage IV Non–Small-Cell Lung Cancer Data from 1,066 patients recruited from nine European centers ...