nonparametric regression). Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely used for...

linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where...

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

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

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

In regression analysis, a dummy variable (also known as indicator variable or just dummy) is one that takes a binary value (0 or 1) to indicate the absence...

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

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

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

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

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...

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

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

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

Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated...

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

Analysis of covariance (ANCOVA) is a general linear model that blends ANOVA and regression. ANCOVA evaluates whether the means of a dependent variable...

Regression testing (rarely, non-regression testing) is re-running functional and non-functional tests to ensure that previously developed and tested software...

linear regression). Bivariate analysis can be contrasted with univariate analysis in which only one variable is analysed. Like univariate analysis, bivariate...

Partial least squares (PLS) regression is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes...

to the same analysis. Certain types of problems involving multivariate data, for example simple linear regression and multiple regression, are not usually...

Nonparametric regression is a category of regression analysis in which the predictor does not take a predetermined form but is constructed according to...

time-varying covariates. The Cox PH regression model is a linear model. It is similar to linear regression and logistic regression. Specifically, these methods...

Meta-regression is defined to be a meta-analysis that uses regression analysis to combine, compare, and synthesize research findings from multiple studies...

In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic...

The method of least squares is a parameter estimation method in regression analysis based on minimizing the sum of the squares of the residuals (a residual...

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

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

squares Simple linear regression Trend estimation Ridge regression Polynomial regression Segmented regression Nonlinear regression Generalized linear models...

Density Based Empirical Likelihood Ratio tests In regression analysis, more specifically regression validation, the following topics relate to goodness...