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multicollinearity

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multicollinearity [‚məl·tē·kō‚lin·ē′ar·əd·ē]
(statistics)
A concept in regression analysis describing the situation where, because of the high degree of correlation between two or more independent variables, it is not possible to separate accurately the effect of each individual independent variable upon the dependent variable.


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To ensure that the assumptions of multiple regression were met, scatter-plots of residuals against the model data set were inspected, as were tolerance values and the variance inflation factor for possible problems with outliers, influential data points, and multicollinearity.
Due to multicollinearity, large correlations between predictors would dramatically reduce a regression model's stability.
However, the correlations between study variables in Table 1 suggest little evidence of multicollinearity.
 
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