Multicollinearity in regression models is a theoretical condition defined by high inter-correlation among independent variables, which renders individual variable coefficients indistinguishable and unstable within Ordinary Least Squares estimation. This phenomenon occurs when one predictor can be linearly predicted from others with high accuracy, violating the assumption of orthogonality required for valid causal inference regarding specific factors while often leaving predictive performance intact. The core mechanism dictates that reliable assessment of independent effects is only possible in the absence of multicollinearity, necessitating diagnostic thresholds such as tolerance below 0.1 or Variance Inflation Factor (VIF) above 10 to identify instability.
Multicollinearity in regression models is a theoretical condition defined by high inter-correlation among independent variables, which renders individual variable coefficients indistinguishable and u…