multicollinearity


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multicollinearity

(ˌmʌltɪkəʊˌlɪnɪˈærɪtɪ)
n
(Statistics) statistics the condition occurring when two or more of the independent variables in a regression equation are correlated
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Noun1.multicollinearity - a case of multiple regression in which the predictor variables are themselves highly correlated
statistics - a branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parameters
multiple correlation, multiple regression - a statistical technique that predicts values of one variable on the basis of two or more other variables
References in periodicals archive ?
Due to the inclusion of many complementary indicators into the MIP system, the issue of potential multicollinearity ought to be tackled.
A solution for multicollinearity in stochastic frontier production function models
In the presence of multicollinearity among explanatory variables, OLSE becomes unstable and shows undesirable properties, such as inflated variance, wide confidence intervals which leads to wrong inferences and sometimes it even produces wrong signs of the estimates.
Section 3 presents the calculation of carbon emissions from the power sector, the basic STIRPAT model and its extended form, and the PLS method to manage multicollinearity. Section 4 describes the main results and provides a discussion and Section 5 is conclusions and potential policy implications.
Evaluation of multicollinearity among predictor variables was assessed using chi-square test for categorical variables (p < 0.05) and Pearson product-moment correlation for continuous variables (cor [greater than or equal to] 0.5).
(2) There was high multicollinearity between liver function of Child-Pugh Class and Tbil, Alb, and PT (eigenvalue = 0.07=0, condition index = 20.484 > 10; Table 1).
"When moderate to high intercorrelations among the predictors, as is the case when several cognitive measures are used as predictors, the problem is referred to as multicollinearity" (Pituch & Stevens, 2016, pp.
Secondly, the multicollinearity problem results from the fact that the explanatory variables of the diagnostic model are highly correlated, making the regression coefficient estimates unreliable.
For example, there is a phenomenon called multicollinearity issue.
In the regression analysis, when the number of independent variables and the dependent variables are many, and in the independent and dependent variables are among the more serious multicollinearity, if the general multivariate regression method, the reliability analysis is very low, and by partial least squares regression analysis modeling method can well solve the problem.
Multiple regression analysis assumes the potential of multicollinearity. To test for multicollinearity, the researchers examined the variance inflation factor (VIF) and tolerance for the independent variable (social media outreach efforts) in regression analyses where the covariates were present.
(1985) suggest that multicollinearity problems arise only when the correlations among explanatory variables are higher than 0.8.