Orthogonality and Least Squares Method

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Covers orthogonal vectors, unit vectors, and the Pythagorean theorem in R^m.

Explores orthogonality, dot product properties, vector norms, and angle definitions in vector spaces.

Explores orthogonality, norms, and distances in vector spaces for solving linear systems.

Covers the properties of vectors, including commutativity, distributivity, and linearity.

Covers linear applications, diagonalizable matrices, eigenvectors, and orthogonal subspaces in R^n.

Explores the best quadratic approximation in Euclidean spaces, emphasizing least squares.

Covers scalar products, orthogonal vectors, norms, and projections in vector spaces, emphasizing orthonormal families of vectors.

Introduces orthogonality between vectors, angles, and orthogonal complement properties in vector spaces.

Explores signal approximation, orthogonal bases, Fourier series, correlation, and vector geometry.

Explores orthogonal matrices and transformations, emphasizing preservation of norms and angles.