Explore: Regression Models

regression is a model that estimates the relationship between a scalar response (dependent variable) and one or more explanatory variables (regressor

non-linear models (e.g., nonparametric regression). Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis

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

Poisson heterogeneity with a gamma distribution. Poisson regression models are generalized linear models with the logarithm as the (canonical) link function

hazards model can itself be described as a regression model. There is a relationship between proportional hazards models and Poisson regression models which

general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that

In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is

In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than

linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be

These models can be seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models. These