Imperfect multicollinearity refers to a situation in regression analysis where two or more independent variables are highly correlated, but not perfectly. This leads to challenges in estimating the coefficients accurately and can affect the reliability of statistical inferences made from the model. Understanding imperfect multicollinearity is crucial as it can cause inflated standard errors and unstable estimates, making it difficult to determine the individual effect of each independent variable on the dependent variable.
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