Procedures for Estimating Standardized Regression Coefficients From Sample Data

Datos Bibliográficos

ID2331271
AutoresLawrence S Mayer (Department of Statistics Virginia Polytechnic Institute and State University), Mary Sue Younger (Department of Statistics Virginia Polytechnic Institute and State University)
Año1974
Volumen2
Número4
Páginas431-453
Fecha de publicación1974-05-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaSociological Methods & Research (JOURNAL)
Identificadores de la revistaISSN: 0049-1241 • E-ISSN: 1552-8294
EditorialSAGE Publications Inc (PUBLISHER)
DOI10.1177/004912417400200403
OpenAlexW1967686013
IdiomaEN
Citas recibidas4
Referencias citadas7

This paper is concerned with the role played by the standardized regression coefficients in linear regression analysis. The linear regression model is reparameterized to explicitly contain standardized regression coefficients. Several estimators of these coefficients are considered. It is shown that the usual beta coefficient is a good estimator of the coefficients in the linear regression model with random predictor variables. However, in the linear regression model with nonstochastic predictors, alternative estimators are better than the usual beta coefficient. A sociological application is included in order to display the empirical behavior of the various estimators

Bayesian multivariate linear regression · Cross-sectional regression · Econometrics · Estimator · Linear model · Linear predictor function · Linear regression · Local regression · Polynomial regression · Proper linear model · Regression · Regression analysis · Regression diagnostic · Regression dilution · Sample (material) · Standardized coefficient · Statistics · Advanced Statistical Methods and Models · Mathematics