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 ...

Estimating Coefficients and Predicting Values The equation y = mx +b represents the most basic linear regression equation: x is the predictor or independent variable y is the dependent variable or ...

The Data Science Lab How to Do Kernel Logistic Regression Using C# Dr. James McCaffrey of Microsoft Research uses code samples, a full C# program and screenshots to detail the ins and outs of kernal ...

Colin B. Begg, Robert Gray, Calculation of Polychotomous Logistic Regression Parameters Using Individualized Regressions, Biometrika, Vol. 71, No. 1 (Apr., 1984), pp ...

Lawless and Singhal (1978, Biometrics 34, 318-327) proposed a method for best subsets selection for nonnormal models. We develop a method for logistic regression that may be performed with any best ...

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 ...