 Solution

Let $v_2,v_3,v_4$ be the three supplied coefficient vectors, in their displayed order. Multiplication by the sample covariance matrix givesThe four vectors are pairwise orthogonal, with norms $\sqrt{3}$, $\sqrt{102}$, $\sqrt{6}$ and $\sqrt{204}$. Hence $u_j=v_j/\Vert v_j\Vert$ form an orthonormal basis of eigenvectors. The variance of the principal component with unit coefficient vector $u_j$ is its eigenvalue ${\lambda }_j$. Using unnormalized coefficient vectors would instead give variances ${\lambda }_j\Vert v_j{\Vert }^2$ and would not produce the desired proportions.

The total variance isTherefore the explained variance of a principal component isThe first two principal components together account for $2658/3162\simeq 84.06\%$, and the first three account for $2958/3162\simeq 93.55\%$ of the total variance.