can data reduction techniques be applied when two variables are highly correlated how to deal with multicollinearity without dropping variables why do we lose explainability why are variables dropping in the model when analyzing data? why would we lose explainability why should two variables be kept in a linear model why are vifs in a linear model high how to deal with multicollinearity without dropping variables why does pca make a model more complicated which data reduction technique to use what is multicollinearity and how to deal with it can you drop the models and models does multicollinearity affect predicting models how to handle multicollinearity without dropping variables how to solve multicollinearity if two variables are highly correlated, then why is explainability low? when two variables in a linear model are highly correlated, what happens why should vif be kept in model how does pca affect explainability what is multicollinearity without dropping variables