# 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
ThesaurusAntonymsRelated WordsSynonymsLegend:
 Noun 1 multicollinearity - a case of multiple regression in which the predictor variables are themselves highly correlatedstatistics - a branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parametersmultiple correlation, multiple regression - a statistical technique that predicts values of one variable on the basis of two or more other variables
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Multicollinearity was diagnosed based on the number of conditions (NC), by the ratio of the highest by the lowest eigenvalue of the matrix.
Second, the addition of parameter updating helps to avoid possible multicollinearity problems that might arise when econometric equations are used to generate the model variables (in this case, proxies of expectations).
Multicollinearity occurs when more than two predictor variables are inter-correlated.
In addition, whenever regression variables are employed, there is a probability of the presence of multicollinearity within the set of independent variables which may be problematic from an interpretive perspective.
Baron and Kenny (1986) find that an ANOVA test is effective in that it precludes the concept of multicollinearity that sometimes arises in regression results, along with measurement error.
As regards multiple correlation, we first set a correlation matrix to determine mutual relationships between the independent variables; see multicollinearity above.
To address problems related to autocorrelation and multicollinearity in the hierarchical data associated with the construction of taper models, we used appropriate statistical procedures for the model fitting.
Currently, several methods may be used to diagnose multicollinearity, and calculating tolerance (TOL) and variance inflation factor (VIP) are the most popular methods of diagnosing multicollinearity as proposed in literature.
Subsequently, the multicollinearity diagnosis was performed by the condition number analysis (CN), which represents the ratio between the largest and the smallest eigenvalue of the correlation matrix.
The multicollinearity is verified by the Pearson correlation and the variance inflation factor (VIF).
Due to the multicollinearity problem, ridge regression analysis was preferred instead of multivariate linear regression for statistical evaluations to define a model to predict skeletal age based on C3_H and C4_H.
Estimates of the path coefficients needed to measure the direct and indirect effects of the characteristics analyzed on yield were performed under the effect of multicollinearity by using ridge path analysis, in which a constant (k) is added to the diagonal elements of the Matrix X'X.

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