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

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

regression; a model with two or more explanatory variables is a multiple linear regression. This term is distinct from multivariate linear regression...

called regressors, predictors, covariates, explanatory variables or features). The most common form of regression analysis is linear regression, in which...

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

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

In statistics, ordinal regression, also called ordinal classification, is a type of regression analysis used for predicting an ordinal variable, i.e....

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

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

Look up regression, regressions, or régression in Wiktionary, the free dictionary. Regression or regressions may refer to: Regression (film), a 2015 horror...

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...

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 consistent...

models are three special cases of functional nonlinear regression models. Functional polynomial regression models may be viewed as a natural extension...

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

Ridge regression (also known as Tikhonov regularization, named for Andrey Tikhonov) is a method of estimating the coefficients of multiple-regression models...

Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable...

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

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

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

Disease Control and Prevention (CDC). Apache 2 licensed Fityk – nonlinear regression software (GUI and command line) GNU Octave – programming language...

intersection Line fitting Nonlinear least squares Regularized least squares Simple linear regression Partial least squares regression Linear function Weisstein...

regression analysis, are acceptable as descriptions of the data. The validation process can involve analyzing the goodness of fit of the regression,...

Quantile regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares estimates the conditional...

Linear and Nonlinear Regression: A Practical Guide to Curve Fitting. Oxford University Press. ISBN 978-0-19-803834-4.[page needed] Regression Analysis By...

However, the model can be extended to nonlinear relationships. Particularly, when the mean part of the level 1 regression equation is replaced with a non-linear...

areas such as regression analysis, curve fitting and data modeling. The least squares method can be categorized into linear and nonlinear forms, depending...

In statistics, semiparametric regression includes regression models that combine parametric and nonparametric models. They are often used in situations...

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

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

maximum likelihood estimates of a generalized linear model, and in robust regression to find an M-estimator, as a way of mitigating the influence of outliers...