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Correlation among two or more variables. In regression analysis, high multicollinearity among the independent variables complicates modeling and will compromise the reliability of the resulting coefficients. If the multicollinearity is perfect, the multiple regression algorithms simply will not work and either an error message may result or the software may purge one or more of the problem variables.