How to calculate Principal Component Analysis (PCA) using PCA Methdology? Example Data 2.5 0.5 2.2 1.9 3.1 2.3 2 1 1.5 1.1 2.4 0.7 2.9 2.2 3 2.7 1.6 1.1 1.6 0.9 . PCA Methodology Given a dataset with n observations with m attributes: Step 1: Calculate the mean of each attribute Step 2: From each value, subtract the mean of the attribute Step 3: Calculate the covariance matrix Step 4: Compute the eigen values of the covariance matrix; order them from largest to smallest Step 5: Compute Eigen vectors of the covariance matrix Step 6: Construct new dataset with new variables Step 7: Dimensionality reduction step. Keep terms corresponding to the Klargest Eigen values