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

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

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...

{\displaystyle y} -intercept The least squares regression line is a method in simple linear regression for modeling the linear relationship between two variables...

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...

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

method for robustly fitting a line to sample points in the plane (simple linear regression) by choosing the median of the slopes of all lines through pairs...

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

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

Nonlinear least squares Regularized least squares Simple linear regression Partial least squares regression Linear function Weisstein, Eric W. "Normal Equation"...

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...

from the linear regression to the result from the t-test. From the t-test, the difference between the group means is 6-2=4. From the regression, the slope...

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

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...

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

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

(SSR :the sum of squares due to regression or explained sum of squares), is generally true in simple linear regression: ∑ i = 1 n ( y i − y ¯ ) 2 = ∑ i...

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

several definitions of R2 that are only sometimes equivalent. In simple linear regression (which includes an intercept), r2 is simply the square of the sample...

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

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

adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. It is a non-parametric regression technique...

regression coefficient relating the y t {\displaystyle y_{t}} ′s to the actually observed x t {\displaystyle x_{t}} ′s, in a simple linear regression...

to compute than the simple linear regression. Most statistical software packages used in clinical chemistry offer Deming regression. The model was originally...

column vector of ones. This section gives an example of simple linear regression—that is, regression with only a single explanatory variable—with seven observations...

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

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

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

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