Given a data frame d with 100 rows and 5 numeric columns, the following statements were...

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Given a data frame d with 100 rows and 5 numeric columns, the following statements were executed in an R session: p - prcomp (d); u1 <- psrotation[, 1]; u2 <- p$rotation[, 2]; m <- cov(d); Key facts about the functions used and objects generated here: The prcomp function performs a Principal Component Analysis (PCA). The cov function computes a covariance matrix. The diag function extracts the elements of the diagonal from a matrix. The columns of the rotation matrix in psrotation contain the principal components. pssdey contains the standard deviations for the principal components. Check all statements that are mathematically correct after running the code above. ● ● T • CH sum(u1u1) will evaluate to 1. U sum(pssdevpssdev) - sum(diag(m)) will evaluate to e sum(diag(p$rotation)) will evaluate to 1. sum(diag(m)) will evaluate to a 4 Previous 90% Next > Reset Given a data frame d with 100 rows and 5 numeric columns, the following statements were executed in an R session: p - prcomp (d); u1 <- psrotation[, 1]; u2 <- p$rotation[, 2]; m <- cov(d); Key facts about the functions used and objects generated here: The prcomp function performs a Principal Component Analysis (PCA). The cov function computes a covariance matrix. The diag function extracts the elements of the diagonal from a matrix. The columns of the rotation matrix in psrotation contain the principal components. pssdey contains the standard deviations for the principal components. Check all statements that are mathematically correct after running the code above. ● ● T • CH sum(u1u1) will evaluate to 1. U sum(pssdevpssdev) - sum(diag(m)) will evaluate to e sum(diag(p$rotation)) will evaluate to 1. sum(diag(m)) will evaluate to a 4 Previous 90% Next > Reset

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Given statements sumui ui will evaluate to the variance of the first principal component sumpssdev p... View the full answer