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1. Multicollinearity occurs when there is a strong linear relationship between two or more predictor variables in a regression model. This can result in unstable and unpredictable estimates of regression coefficients. 2. The consequences of multicollinearity include estimates remaining best linear unbiased estimates but having larger standard errors and wider confidence intervals, as well as insignificant coefficients and highly sensitive estimates. 3. Multicollinearity can be detected by having a high

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