Is an × matrix. check the true statements below: a. if and are linearly independent eigenvectors, then they correspond to distinct eigenvalues. b. any non-zero vector in the nullspace of a square matrix is an eigenvector. c. if = for some non-zero vector x, then x is an eigenvector of d. the eigenvalues of a matrix are on its main diagonal. in eigenspace of is just a null space of a certain matrix.

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step-by-step explanation:

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ok so the answer to your question is simple as long as you look at the different dimensions that contain the exemplary letters that combined in the right order give you words that eventually within the 5th dimension will conform and give you the correct answer to your question. hope i , stop looking at this easily, look at it with a spectral view.

step-by-step explanation:

told you

one set of data has more variation than the other. hope this !