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

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

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

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

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

application software SegReg is a free and user-friendly tool for linear segmented regression analysis to determine the breakpoint where the relation between the...

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

data with random variation the tolerance level can be found with segmented regression. As the Maas-Hoffman model is fitted to the data by the method of...

Bockerman et al. (2018). Note that regression kinks (or kinked regression) can also mean a type of segmented regression, which is a different type of analysis...

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

Autoregressive Integrated Moving Average (ARIMA) models are an alternative to segmented regression that can also be used for fitting a moving-average model....

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

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...

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

combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model...

yield depression The figure was made with the SegReg program for segmented regression. In 1991 a closed-form expression was developed for the equivalent...

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

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

piecewise linear or segmented function is a real-valued function of a real variable, whose graph is composed of straight-line segments. A piecewise linear...

aggregate behavior (for example, public opinion). The models used in segmented regression analysis are threshold models. Certain deterministic recursive multivariate...

multilevel regression with poststratification model involves the following pair of steps: MRP step 1 (multilevel regression): The multilevel regression model...

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

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

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

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

altered. Linear least squares Linear segmented regression Linear trend estimation Polynomial regression Regression dilution "Fitting lines", chap.1 in...

distinction is most important in regression analysis, where the concepts are sometimes called the regression errors and regression residuals and where they lead...

e-cigarettes renormalised or displaced youth smoking? Results of a segmented regression analysis of repeated cross sectional survey data in England, Scotland...

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

the preference datum. Like all regression methods, the computer fits weights to best predict data. The resultant regression line is referred to as an ideal...