A logistic regression for these data is a generalized linear model with response equal to the binomial proportion r/n. The probability distribution is binomial, and the link function is logit.

The response variable y is ordinally scaled. A cumulative logit model is used to investigate the effects of the cheese additives on taste. The following SAS statements invoke PROC LOGISTIC to fit this ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

Peiming Wang, Martin L. Puterman, Mixed Logistic Regression Models, Journal of Agricultural, Biological, and Environmental Statistics, Vol. 3, No. 2 (Jun., 1998), pp ...

Parametric empirical Bayes methods are discussed for estimating the mean proportion response from generalized linear regression models (GLiM's) based on the binomial distribution, including the ...

Regression using step and logistic models yields thresholds of 185 cm (solid vertical blue line) and 194 cm (dashed blue line), respectively.