Multicollinearity can lead to several issues in regression analysis. The primary concerns include:
Inflated Variance: The standard errors of the coefficients can become very large, making it difficult to assess the significance of the predictors. Unstable Coefficients: Small changes in the data can lead to large changes in the estimated coefficients, leading to unreliable models. Redundancy: It becomes challenging to determine the individual effect of each predictor variable since they convey overlapping information.
