Add to ORKGThe precision of the estimates of the regression coefficients in a regression analysis is affected by multicollinearity. The effect of certain factors on multicollinearity and the estimates was studied. The response variables were the standard error of the regression coefficients and a standarized statistic that measures the deviation of the regression coefficient from the population parameter. The estimates are not influenced by any one factor in particular, but rather some combination of factors. The larger the sample size, the better the precision of the estimates no matter how bad the other factors may be. The standard error of the regression coefficients proved to be the best indication of estimation problems
In regression, the objective is to explain the variation in one or more response variables, by assoc...
While multicollinearity may increase the difficulty of interpreting multiple regression results, it ...
This article argues that rather than using one technique to investigate regression results, research...
The precision of the estimates of the regression coefficients in a regression analysis is affected b...
In regression analysis it is obvious to have a correlation between the response and predictor(s), bu...
The adverse impact of ignoring multicollinearity on findings and data interpretation in regression a...
The present Monte Carlo simulation study adds to the literature by analyzing parameter bias, rates o...
When the multicollinearity among the independent variables in a regression model is due to the high ...
Abstract. Multicollinearity in empirical data violates the assumption of independence among the regr...
Multicollinearity in linear regression is typically thought of as a problem of large standard errors...
The paper reviews some of the applications of the regression technique and points out possible pitfa...
Multicollinearity in empirical data violates the assumption of independence among the regressors in ...
One of the main goals of the multiple linear regression model, Y = Xβ + u, is to assess the importan...
Multicollinearity has remained a major problem in regression analysis and should be sustainably addr...
The performances of two biased estimators for the general linear regression model under conditions o...
In regression, the objective is to explain the variation in one or more response variables, by assoc...
While multicollinearity may increase the difficulty of interpreting multiple regression results, it ...
This article argues that rather than using one technique to investigate regression results, research...
The precision of the estimates of the regression coefficients in a regression analysis is affected b...
In regression analysis it is obvious to have a correlation between the response and predictor(s), bu...
The adverse impact of ignoring multicollinearity on findings and data interpretation in regression a...
The present Monte Carlo simulation study adds to the literature by analyzing parameter bias, rates o...
When the multicollinearity among the independent variables in a regression model is due to the high ...
Abstract. Multicollinearity in empirical data violates the assumption of independence among the regr...
Multicollinearity in linear regression is typically thought of as a problem of large standard errors...
The paper reviews some of the applications of the regression technique and points out possible pitfa...
Multicollinearity in empirical data violates the assumption of independence among the regressors in ...
One of the main goals of the multiple linear regression model, Y = Xβ + u, is to assess the importan...
Multicollinearity has remained a major problem in regression analysis and should be sustainably addr...
The performances of two biased estimators for the general linear regression model under conditions o...
In regression, the objective is to explain the variation in one or more response variables, by assoc...
While multicollinearity may increase the difficulty of interpreting multiple regression results, it ...
This article argues that rather than using one technique to investigate regression results, research...