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 ?
It was concluded that there is no multicolinearity problem in the fertility equation, which is the main focus of this paper.
(6)As none of the zero-order correlations among this study's independent and control variables exceed r = 0.53, high multicolinearity is not a problem for the estimation of the unique effects of each of these variables accomplished through the regression equations solved.
Ridge Regression (RR): Since the RHS variables in Equation (1) are all exogenous, it may appear that a simple ordinary least squares (OLS) technique will be sufficient to produce unbiased estimates for the parameters of SPRE However, since some of the RHS variables in Equation (1) [e.g., GNP, INF, UNM] are expected to be correlated with each other, this may pose a problem of multicolinearity if an OLS method is adopted to estimate Equation (1).
(28.) The negative sign for the mudslinging variable is not the result of multicolinearity with the advertising and news coverage variables.
Any regression model is likely to encounter some degree of multicolinearity and the present model is no exception.
And then to find a way of overcoming the econometric problem of multicolinearity which is usually encountered in cases like this between real income and the monetization variable.
We found a good deal of multicolinearity between some of the variables, however, most notably a .55 correlation between the closeness of the Senate race and its tone.
To avoid multicolinearity, dummies for one industry and one province are excluded from the regression.
Multicolinearity produces inefficient estimates, which may lead researchers to accept the null hypothesis when they should not.
Ideally, the percentage of House and Senate moderates would be included in the analysis as separate independent variables, but the two series are highly colinear (Pearson's r = .88 over the past half-century), which would introduce an unacceptable level of multicolinearity into the analysis.
In our legislative and presidential data sets, the respective correlations between the two variables are only --.29 and --.44, both short of the level at which multicolinearity becomes a serious problem.
The critics have demonstrated that including a trend term produces severe multicolinearity in Abramson, Ellis, and Inglehart's (1997) regression analyses of the effects of unemployment and inflation.