Can Principal Component Scores Be Used As Predictors in Regression Analysis After Pca?
After applying principal component analysis (PCA), the resulting principal component scores can be used as independent variables in a regression model to predict the dependent variable. This approach facilitates dimensionality reduction, mitigates multicollinearity among predictors, and enhances the robustness of the regression model. However, caution is required when interpreting the relationship between the principal component scores and the dependent variable, as the scores are linear combinations of the original variables and may lack direct interpretability.
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