Multicollinearity in regression analysis refers to the extent to which the independent variables are redundant.

Name: Multicollinearity in regression analysis refers to the extent to which the independent variables are redundant.
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Multicollinearity occurs when the independent variables in a regression model are highly correlated with each other, indicating redundancy or overlap in their explanatory power. It can lead to unstable parameter estimates and decreased predictive accuracy.

Multicollinearity in Regression Analysis Refers to the Extent to Which