 Principal component regression

Principal Component Regression (PCR) is a statistical technique used in regression analysis that combines the principles of principal component analysis (PCA) with linear regression. It is particularly useful when dealing with multicollinearity, which occurs when independent variables in a regression model are highly correlated, leading to unstable coefficient estimates and reduced interpretability.