Problem 1
They identify orthogonal directions of variation; eigenvalues give variance along those directions.
Faculty use
Principal Axes, Covariance & Geometric Interpretation
Reward correct mathematics, explicit dimensions/hypotheses, interpretation, and verification. Equivalent valid reasoning should receive credit.
Problem 2
Scaling, centering, units, outliers, and whether a linear covariance model is appropriate.
Problem 3
Definitions should identify the vector/matrix/space/transformation, dimensions or domain/codomain, and the hypotheses behind rotating coordinates to uncouple variation and curvature. Use the formulas covariance matrix is symmetric positive semidefinite; eigenvectors give principal directions.
Problem 4
A strong error analysis identifies the first step that violates a definition, dimension, theorem hypothesis, or matrix-operation rule; then repairs that step and re-verifies the final result.
Problem 5
Application responses vary. Require defined variables, a compatible matrix/vector model connected to energy models, covariance, optimization, conic classification, stability, and mechanics, a complete computation, interpretation, and limitations.
Problem 6
Accept two independent checks appropriate to the task: reconstruction, residual, RREF comparison, determinant/rank test, orthogonality, substitution, eigenpair check Av=λv, factorization product, or a second method.
Scoring guidance
Six problems × 10 points: 5 mathematical accuracy, 2 structure/justification, 2 verification/interpretation, 1 notation and communication.