multicollinearity

(ˌmʌltɪkəʊˌlɪnɪˈærɪtɪ)

(Statistics) statistics the condition occurring when two or more of the independent variables in a regression equation are correlated

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Noun

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

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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.

Analysing the relevance of the MIP scoreboard's Indicators

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.

A Note on the Performance of Biased Estimators with Autocorrelated Errors

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).

Factors Affecting Herd Status for Bovine Tuberculosis in Dairy Cattle in Northern Thailand

"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.

The Role of Recreational Therapy in the Treatment of a Stroke Population

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.

Analysis of partial least squares modeling and multiple collinear ability evaluation: theoretical deduction and case analysis

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.

Social media marketing: utilizing social media to advance brand awareness and increase online sales

(1985) suggest that multicollinearity problems arise only when the correlations among explanatory variables are higher than 0.8.

The impact of the great recession on S&P 500 firms and their marketing efforts: an empirical analysis