Given data fame X that consists of 6 data objects, each with 2 attributes 2 13 1. [1pt] Convert data frame into centered matrix 2. [1pt Compute covariance matrix Cx (with estimated sample mean version) 3. [1pt] Compute characteristic polynomial of C and eigenvalues of G 4. [1pt find principal components/rotation matrix 5. [1pt] how much variance (%) is explained by new first principal component y,? 6 [1pt] Compute PCA transformation (rotation) of X (without centering). Given data fame X that consists of 6 data objects, each with 2 attributes 2 13 1. [1pt] Convert data frame into centered matrix 2. [1pt Compute covariance matrix Cx (with estimated sample mean version) 3. [1pt] Compute characteristic polynomial of C and eigenvalues of G 4. [1pt find principal components/rotation matrix 5. [1pt] how much variance (%) is explained by new first principal component y,? 6 [1pt] Compute PCA transformation (rotation) of X (without centering)