multicollinearity
/ˌmʌltikəˌlɪniˈɛrəti/
noun
- A statistical condition in which two or more predictor variables in a regression model are highly correlated, making it difficult to determine the individual effect of each variable.
- High multicollinearity can inflate the standard errors of the coefficients.
- The data analyst checked for multicollinearity before building the regression model.
- To reduce multicollinearity, the researcher removed one of the highly correlated variables.