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

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

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

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

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...

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...

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

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

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

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

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

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

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

analysis has continuous independent variables and a categorical dependent variable (i.e. the class label). Logistic regression and probit regression are...

Also, regression analysis assigns a "weight" to each factors that identifies how much it affects the outcome of the event. Regression analysis has become...

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

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...

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

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

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

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

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

Symbolic regression (SR) is a type of regression analysis that searches the space of mathematical expressions to find the model that best fits a given...

Nonparametric regression is a form of regression analysis where the predictor does not take a predetermined form but is completely constructed using information...

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...

characterized. Step 1 and step 2 use simple regression analysis, whereas step 3 uses multiple regression analysis. How you were parented (i.e., independent...

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