In statistics, unit-weighted regression is a simplified and robust version (Wainer & Thissen, 1976) of multiple regression analysis where only the intercept...

of combining scores has come to be called the Burgess method of unit-weighted regression. Hakeem (1948) reported that the Burgess method had "remarkable...

Weighted least squares (WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge...

weighted least squares. Its square root is called regression standard error, standard error of the regression, or standard error of the equation (see Ordinary...

and the criterion. Simple regression analysis is the most common example of a proper linear model. Unit-weighted regression is the most common example...

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

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

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

labeled as "b". Linear regression Correlation coefficient Effect size Unit-weighted regression Menard, S. (2004), "Standardized regression coefficients", in...

solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals. Numerical...

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

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

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

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

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

some stats context) Unimodality Unit (statistics) Unit of observation Unit root Unit root test Unit-weighted regression Unitized risk Univariate Univariate...

In statistics, a probit model is a type of regression where the dependent variable can take only two values, for example married or not married. The word...

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

making, including models with equal weights, a method known as unit-weighted regression. He co-wrote an early textbook on mathematical psychology (see...

data-sources; however the regression procedure takes no account for possible errors in estimating this ratio. The Deming regression is only slightly more...

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

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

the initial estimator. Robust confidence intervals Robust regression Unit-weighted regression Sarkar, Palash (2014-05-01). "On some connections between...

parameters in a linear regression model. It is used when there is a non-zero amount of correlation between the residuals in the regression model. GLS is employed...

linear regression models. This simple case reveals a substantial amount about the estimator. These include its relationship to ridge regression and best...

can be seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models. These models became...

his work on multivariate statistics. He also conducted work on unit-weighted regression, proving the idea that under a wide variety of common conditions...

may have a unit root, as discussed above. The finite sample properties of regression models with first order ARMA errors, including unit roots, have...

as the least angle regression algorithm. One of the prime differences between Lasso and ridge regression is that in ridge regression, as the penalty is...