👉 Multivariate Principal Component Regression (MPC-R) is a statistical modeling technique that combines the dimensionality reduction capabilities of Principal Component Analysis (PCA) with the regression framework of multiple linear regression. MPC-R first applies PCA to a set of predictor variables, transforming them into a smaller set of uncorrelated principal components that capture the maximum variance in the data. These components are then used as inputs in a regression model to predict a response variable, allowing for the simultaneous consideration of multiple predictors while reducing multicollinearity and improving model interpretability. This approach is particularly useful when dealing with high-dimensional data, as it helps in identifying the most significant patterns and reducing noise, leading to more robust and efficient predictive models.
