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...

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

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

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

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

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...

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...

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

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

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

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

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

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

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

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

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

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

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

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

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

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...

(2018) shows, several shrinkage estimators – such as Bayesian linear regression, ridge regression, and the (adaptive) lasso – make use of this decomposition...

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

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

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

Regression dilution, also known as regression attenuation, is the biasing of the linear regression slope towards zero (the underestimation of its absolute...

the probit regression, (ii) threshold regression, (iii) smooth regression, (iv) logistic link regression, (v) Box–Cox transformed regressors ( m ( x ,...

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