multicolinearity

mul·ti·col·in·e·ar·i·ty

(mul'tē-kol'in-ē-ar'i-tē),
In multiple regression analysis, a situation in which at least some independent variables in a set are highly correlated with each other.
[multi- + L. col-lineo, to line up together]
References in periodicals archive ?
To reduce multicolinearity between the main effect and interaction terms, all independent and dependent variables were standardized.
Estimating body weight from several body measurements in Harnai sheep without multicolinearity problem.
To determine whether multicolinearity would have a detrimental effect on our results from the regression analyses, we examined tolerance and VIF statistics on our four equations.
However, for observing the multicolinearity and heteroscedasticity problem in the data we employed variance inflation (VIF) tolerance levels and group wise heteroscedasticity tests through statistical package STATA 13.
Statistical tests were performed to avoid violation of any statistical assumptions including normality, multicolinearity and homoscedasticity to run mediation, correlation and regression analyses.
Matrices: the software operates algebraic matrices, diagnosis of multicolinearity in correlation matrices and obtains general solution from linear regression models.
The VIF test (variance inflation factor) did not identify any multicolinearity problems, while the autocorrelation and heteroscedasticity tests identified these problems alternately throughout the nine estimations, as presented in Table 2.
Additionally, some multicolinearity is desirable because the objective of the factor analysis is precisely to identify sets of inter-related variables (Hair et al.
The variance inflation factor and multicolinearity were assessed for each model and the result indicated no predictors needed to be removed from the models.
First-best policy would be to expand the dataset, sampling 20 % of all populations or exploring more villages, particularly because of multicolinearity problems from the Hindu village of Shahapara.
The upsampled data was then standardized and centred to decrease the multicolinearity between an interaction term and its corresponding main effects as well as making categorical parameters such as gender, comparable with continuous parameters.
Preliminary analyses were conducted to ensure no violation of the assumptions of normality, linearity, multicolinearity and homoscedasticity.