independent variables. In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model (the coefficients...

etc.). Multinomial logistic regression is known by a variety of other names, including polytomous LR, multiclass LR, softmax regression, multinomial logit...

Conditional logistic regression is an extension of logistic regression that allows one to account for stratification and matching. Its main field of application...

distribution plays the same role in logistic regression as the normal distribution does in probit regression. Indeed, the logistic and normal distributions have...

A logistic function or logistic curve is a common S-shaped curve (sigmoid curve) with the equation f ( x ) = L 1 + e − k ( x − x 0 ) {\displaystyle f(x)={\frac...

the cross-entropy loss for logistic regression is the same as the gradient of the squared-error loss for linear regression. That is, define X T = ( 1...

logit model (also ordered logistic regression or proportional odds model) is an ordinal regression model—that is, a regression model for ordinal dependent...

common binary regression models are the logit model (logistic regression) and the probit model (probit regression). Binary regression is principally...

Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes...

In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic...