Investopedia

Stepwise Regression Explained: Uses, Benefits, and Drawbacks

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...
Nature

Variable selection in logistic regression for detecting SNP–SNP interactions: the rheumatoid arthritis example

Many complex disease traits are observed to be associated with single nucleotide polymorphism (SNP) interactions. In testing small-scale SNP–SNP interactions, variable selection procedures in logistic ...
Simon Fraser University

Example 39.1: Stepwise Logistic Regression and Predicted Values

Results of the CTABLE option are shown in Output 39.1.11. Each row of the "Classification Table" corresponds to a cutpoint applied to the predicted probabilities, which is given in the Prob Level ...
technologynetworks

Logistic Regression

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 ...
JSTOR Daily

LOTUS: An Algorithm for Building Accurate and Comprehensible Logistic Regression Trees

Logistic regression is a powerful technique for fitting models to data with a binary response variable, but the models are difficult to interpret if collinearity, nonlinearity, or interactions are ...
Simon Fraser University

Comparison of CATMOD, GENMOD, LOGISTIC, and PROBIT Procedures

The LOGISTIC, GENMOD, PROBIT, and CATMOD procedures can all be used for statistical modeling of categorical data. The CATMOD procedure provides maximum likelihood estimation for logistic regression, ...