(Solved): Part iii please 2. Let X be a mean centered np data matrix. (i) Define the sample covariance matri ...
Part iii please
2. Let X be a mean centered n×p data matrix. (i) Define the sample covariance matrix S in terms of X. (ii) If W be a diagonal matrix with entries Sii? for i?1,…,p and R is the sample correlation matrix, how can R be written in terms of S and W. (iii) Show that the PCA components derived from using the sample covariance matrix S will be equivalent to those derived using the sample correlation matrix R when the variances of the p variables are all equal.
(Principal Component Analysis) is a statistical technique that transforms a set of correlated variables into a smaller set of uncorrelated variables called principal components. The principal components are ordered in such a way that the first component explains the most variance in the data, the second component explains the second most variance, and so on.