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

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

used. (See also multivariate adaptive regression splines.) Penalized splines. This combines the reduced knots of regression splines, with the roughness...

(statistics) Local regression Non-parametric statistics Semiparametric regression Isotonic regression Multivariate adaptive regression splines Cherkassky, Vladimir;...

Linear equation Logistic regression M-estimator Multivariate adaptive regression spline Nonlinear regression Nonparametric regression Normal equations Projection...

involving multivariate data, for example simple linear regression and multiple regression, are not usually considered to be special cases of multivariate statistics...

estimation algorithm) Local regression Modifiable areal unit problem Multivariate adaptive regression spline Multivariate normal distribution Pearson...

In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization...

integral of a similar function or use adaptive routines such as stratified sampling, recursive stratified sampling, adaptive umbrella sampling or the VEGAS algorithm...

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

Standardized covariance Standardized slope of the regression line Geometric mean of the two regression slopes Square root of the ratio of two variances...

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

covariance matrices as the multivariate measure of dispersion. Several authors have considered tests in this context, for both regression and grouped-data situations...

the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function...

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

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

covariance (which reflects the scale variables are measured on). The slope, in regression analysis, also reflects the relationship between variables. The unstandardised...

Ordinary least squares Multivariate adaptive regression splines Local regression Regression discontinuity design Stepwise regression SegReg (software) for...

Analogously to how the median generalizes to the geometric median (GM) in multivariate data, MAD can be generalized to the median of distances to GM (MADGM)...

correspond to the axes of the 1 sd error ellipsoid of the multivariate normal distribution. See Multivariate normal distribution: geometric interpretation. The...

to multiple regression analysis is sometimes used as an aid to interpretation. (page 95) state the following. "The standardized regression slope is the...

estimator has been generalized to multivariate distributions. The Theil–Sen estimator is a method for robust linear regression based on finding medians of slopes...

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

doing regression. Least squares applied to linear regression is called ordinary least squares method and least squares applied to nonlinear regression is...

that a proposed regression model fits the data well. See Lack-of-fit sum of squares. The hypothesis that a data set in a regression analysis follows...

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

linear regression assumptions hold; further we assume that the slope of the covariate is equal across all treatment groups (homogeneity of regression slopes)...

Multitrait-multimethod matrix Multivariate adaptive regression splines Multivariate analysis Multivariate analysis of variance Multivariate distribution – see Joint...

simple function (also called regression). The main difference between regression and interpolation is that polynomial regression gives a single polynomial...

the linear regression to the result from the t-test. From the t-test, the difference between the group means is 6-2=4. From the regression, the slope...