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 ?
The results of the application of the multivariate technique of panel data, taking into consideration the dependent variable and all independent variables, showed the existence of a high correlation between independent variables causing multicollinearity.
Brooks (2008) groups multicollinearity into two, namely perfect and near multicollinearity.
Though this study already applies a maximum R-square improvement procedure, which is a very popular method for combating the multicollinearity (Freund, Wilson 1998) that may impair the usefulness of a model's estimated parameters, there is a need to examine if multicollinearity still exists.
As part of our analysis, we investigate for multicollinearity and provide a correlation table for the 2007, 2008 and 2009 data.
In Chapter 2, the authors introduce simple and multiple regression, data transformations, multicollinearity and ridge regression, and independent variable reduction using principal components analysis.
82)] and there should not be multicollinearity [multicollinearity is a situation in which several independent variables are highly correlated with each other.
Following the recommendations of Tabachnick and Fidell (2007), the data were examined for evidence of multicollinearity before conducting the regression analyses.
The presence of multicollinearity is assessed using both the correlation coefficients and the variance inflations factors.
Other socio-demographic characteristics, including educational level, percentage of the population that is white, and percentage of the population that is uninsured were initially included in the model, but subsequently dropped because of multicollinearity.
3 (four of which are the aforementioned correlations pertaining to firm size), suggesting multicollinearity is not a problem.
We have constructed a correlation matrix of explanatory variables for the detection of Multicollinearity.
The part and partial correlations for both GPAs and scores are almost identical to their respective zero-order correlations, suggesting that multicollinearity is not a serious problem.