Researchers in nanotechnology can adopt several strategies to mitigate multicollinearity:
Remove Redundant Variables: Identifying and eliminating variables that provide similar information can reduce multicollinearity. Principal Component Analysis (PCA): This technique transforms correlated variables into a set of linearly uncorrelated variables called principal components. Regularization Techniques: Methods such as Ridge Regression or Lasso Regression can help manage multicollinearity by adding a penalty to the model.
