explanatory variables (regressor or independent variable). A model with exactly one explanatory variable is a simple linear regression; a model with two or...

In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample...

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

In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination...

Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables...

MANCOVA, ordinary linear regression, t-test and F-test. The general linear model is a generalization of multiple linear regression to the case of more...

in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals. Numerical methods for linear least...

especially in the case of a simple linear regression, in which there is a single regressor on the right side of the regression equation. The OLS estimator is...

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

heteroscedastic errors Simple linear regression, the simplest type of regression, involving only one explanatory variable General linear model for multivariate...

Segmented linear regression is segmented regression whereby the relations in the intervals are obtained by linear regression. Segmented linear regression with...

In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable...

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

an event as a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimates the...

Quantile regression is an extension of linear regression used when the conditions of linear regression are not met. One advantage of quantile regression relative...

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

estimators when linear regression models have some multicollinear (highly correlated) independent variables—by creating a ridge regression estimator (RR)...

squares (PLS) regression is a statistical method that bears some relation to principal components regression and is a reduced rank regression; instead of...

Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its...

In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship...

machine learning, ordinal regression may also be called ranking learning. Ordinal regression can be performed using a generalized linear model (GLM) that fits...

Beta regression is a form of regression which is used when the response variable, y {\displaystyle y} , takes values within ( 0 , 1 ) {\displaystyle (0...

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

squares (WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge of the unequal...

statistics, Bayesian multivariate linear regression is a Bayesian approach to multivariate linear regression, i.e. linear regression where the predicted outcome...

In statistics, regression toward the mean (also called regression to the mean, reversion to the mean, and reversion to mediocrity) is the phenomenon where...

used for estimating the unknown regression coefficients in a standard linear regression model. In PCR, instead of regressing the dependent variable on the...

categorical dependent variable (i.e. the class label). Logistic regression and probit regression are more similar to LDA than ANOVA is, as they also explain...

lowest sampling variance within the class of linear unbiased estimators, if the errors in the linear regression model are uncorrelated, have equal variances...

In statistics and numerical analysis, isotonic regression or monotonic regression is the technique of fitting a free-form line to a sequence of observations...