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...

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

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

= 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...

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

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

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

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

econometrics is the multiple linear regression model. In modern econometrics, other statistical tools are frequently used, but linear regression is still the...

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

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

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

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

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...

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

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...

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

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

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...

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...

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

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

was analyzed using a Pearson's product-moment correlation and a multiple linear regression. Eighty-eight percent of the students in the study reported at...

example, to estimate a mixture of gaussians, or to solve the multiple linear regression problem. The EM algorithm was explained and given its name in...

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

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

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