stages of a regression analysis. Regression diagnostics have often been developed or were initially proposed in the context of linear regression or, more...

Remote diagnostics Root cause analysis Troubleshooting Unified Diagnostic Services Bayesian probability Block Hackam's dictum Occam's razor Regression diagnostics...

Heteroskedasticity Diagnostics for Linear Regression Models". 8 January 2024. "regress postestimation — Postestimation tools for regress" (PDF). Stata Manual...

remaining 51% of the variability is still unaccounted for. For regression models, the regression sum of squares, also called the explained sum of squares,...

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

{1}{1-R_{j}^{2}}},} where Rj2 is the multiple R2 for the regression of Xj on the other covariates (a regression that does not involve the response variable Y) and...

independent variable. Partial residual plots are widely discussed in the regression diagnostics literature (e.g., see the References section below). Although they...

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

statistics, the Ramsey Regression Equation Specification Error Test (RESET) test is a general specification test for the linear regression model. More specifically...

regression does. Linear regression assumes homoscedasticity, that the error variance is the same for all values of the criterion. Logistic regression...

undertakes an auxiliary regression analysis: this regresses the squared residuals from the original regression model onto a set of regressors that contain the...

(1980). Regression Diagnostics. John Wiley. ISBN 0-471-05856-4.{{cite book}}: CS1 maint: multiple names: authors list (link) Partial Regression Plot  This...

regression better fitting values at the ends of the domain. It is also reflected in the influence functions of various data points on the regression coefficients:...

estimates of the regression parameters between the cases, where the particular observation is either included or excluded from the regression analysis. An...

autocorrelation in the errors in a regression model. It makes use of the residuals from the model being considered in a regression analysis, and a test statistic...

Statistics, Outliers, and Collinearity Diagnostics". Belsley, David A.; Kuh, Edwin; Welsh, Roy E. (1980). Regression Diagnostics: Identifying Influential Data...

residual error sum of squares (PRESS) is a form of cross-validation used in regression analysis to provide a summary measure of the fit of a model to a sample...

secondary data sets a regression can be run over the union without the issue of biased estimators arising. Consider the regression: y t = β 0 + β 1 x 1...

Beta regression is a form of regression which is used when the response variable, y {\displaystyle y} , takes values within ( 0 , 1 ) {\displaystyle (0...

DFFITS ("difference in fit(s)") are diagnostics meant to show how influential a point is in a linear regression, first proposed in 1980. DFFIT is the...

context of multiple regression (or univariate regression), the hypothesis to be tested is that the variances of the errors of the regression model are not constant...

useful in working with ARIMA models. In the context of regression analysis, including regression analysis with time series structures, a portmanteau test...

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

regressors, Ypi is the predicted value of the ith observation of Y from the P regressors, S2 is the estimation of residuals variance after regression...

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...

In statistics and in particular in regression analysis, leverage is a measure of how far away the independent variable values of an observation are from...

Regression diagnostic Regression dilution Regression discontinuity design Regression estimation Regression fallacy Regression-kriging Regression model validation...

of that variable in automatic regression model building procedures. Leverage Partial residual plot Partial regression plot Variance inflation factor...

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

Edward Park for estimating linear regression parameters in the presence of heteroscedastic error terms. In regression analysis, heteroscedasticity refers...