Functional regression is a version of regression analysis when responses or covariates include functional data. Functional regression models can be classified...

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

to extending linear regression model to polynomial regression model. For a scalar response Y {\displaystyle Y} and a functional covariate X ( ⋅ ) {\displaystyle...

expansion. FPCA can be applied for representing random functions, or in functional regression and classification. For a square-integrable stochastic process X(t)...

usage metrics of the software after subsequent changes. Unlike functional regression tests, the results of performance tests are subject to variance...

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

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

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

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

Functional Linear Regression, Functional Poisson Regression and Functional Binomial Regression, with the important Functional Logistic Regression included...

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

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

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

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

not limited to: Sanity testing, a.k.a. smoke testing Regression testing Usability testing Functional testing typically involves six steps[citation needed]...

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

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...

algorithms as "functional gradient boosting". Friedman et al. describe an advancement of gradient boosted models as Multiple Additive Regression Trees (MART);...

of multiple functional predictors with a scalar response, the Functional Additive Model can be extended by fitting a functional regression which is additive...

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

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

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

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

parametric (normally polynomial regression). The most common non-parametric method used in the RDD context is a local linear regression. This is of the form: Y...

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

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

regression, has the advantage of not being biased if used on overlapping samples. Another extension of LDSC, known as stratified LD score regression (abbreviated...