Dimension reduction involves projecting the p predictors into an M-dimensional subspace by computing M distinct linear combinations of the variables and utilizing them as predictors for fitting a linear regression model.

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Dimension reduction involves projecting the p predictors into an M-dimensional subspace by computing M distinct linear combinations of the variables and utilizing them as predictors for fitting a linear regression model.

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