The term multicollinearity refers to the condition when the variance of the error term, conditional on x₁, x₂, …, x_n, is the same for all observations.

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The definition given for multicollinearity is incorrect. Multicollinearity refers to the condition when there is a high correlation between two or more predictor variables in a regression model. It can lead to inflated standard errors, unstable coefficients, and unreliable hypothesis tests.

The Term Multicollinearity Refers to the Condition When the Variance