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(Solved): Part iii please 2. Let X be a mean centered np data matrix. (i) Define the sample covariance matri ...



2. Let \( \mathbf{X} \) be a mean centered \( n \times p \) data matrix.
(i) Define the sample covariance matrix \( \mathbf{S

Part iii please

2. Let be a mean centered data matrix. (i) Define the sample covariance matrix in terms of . (ii) If be a diagonal matrix with entries for and is the sample correlation matrix, how can be written in terms of and . (iii) Show that the PCA components derived from using the sample covariance matrix will be equivalent to those derived using the sample correlation matrix when the variances of the variables are all equal.


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   (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.
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