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

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

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

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

= β2 = ⋯ = βk vs. at least one pair βj ≠ βj′ in Multiple linear regression or in Logistic regression. Usually, it tests more than two parameters of the...

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

which it is used today. A basic tool for econometrics is the multiple linear regression model. Econometric theory uses statistical theory and mathematical...

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

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)...

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

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

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

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

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

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

as large. Introduction to Multiple Regression Multiple correlation coefficient Allison, Paul D. (1998). Multiple Regression: A Primer. London: Sage Publications...

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

variables. Multivariate logistic regression uses a formula similar to univariate logistic regression, but with multiple independent variables. π ( x ) =...

the term linear model refers to any model which assumes linearity in the system. The most common occurrence is in connection with regression models and...

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...

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

originally conceived to estimate heteroscedastic error variance in multiple linear regression. MINQUE estimators also provide an alternative to maximum likelihood...

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

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

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

applied statistics and geostatistics, regression-kriging (RK) is a spatial prediction technique that combines a regression of the dependent variable on auxiliary...

can be easily generalised to multiple linear regression. Consider a general linear model as defined in the linear regressions article, that is, Y = X β +...

gradient boosted models as Multiple Additive Regression Trees (MART); Elith et al. describe that approach as "Boosted Regression Trees" (BRT). A popular...

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