Addressing multicollinearity involves several strategies, including:
Variable Selection: Carefully selecting variables that are less likely to be correlated can help mitigate the issue. Principal Component Analysis (PCA): This statistical technique transforms correlated variables into a set of uncorrelated components. Ridge Regression: This method adds a degree of bias to the regression estimates, which can reduce the impact of multicollinearity. Collaboration with Data Scientists: Engaging with data scientists or statisticians can provide valuable insights and technical solutions to handle multicollinearity effectively.