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  • Definition of least-squares solution to a linear system
  • Definition of least-squares error of a linear system
  • Formula for computing the least squares solution to a linear system.
  • Formula for computing the least squares solution to a linear system
  • The least squares solution to a linear system is unique if and only if the columns of the coefficient matrix are linearly independent.
  • Definition of the least-squares linear fit to 2-dimensional data
  • Formula for the least-squares linear fit to 2-dimensional data
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  1. The least squares solution to a linear system is unique if and only if the columns of the coefficient matrix are linearly independent.

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  • The dimension of a direct sum of subspaces is the sum of the dimensions of the subspaces.#23971
  • The dimension of a eigenspace is less than or equal to the (algebraic) multiplicity of the eigenvalue.#23708
  • The dimension of a subspace is less than or equal to the dimension of the whole space#24010
  • The dimension of the domain of an injective linear transformation is at most the dimension of the codomain.#24033
  • The dimension of the domain of a surjective linear transformation is at least the dimension of the codomain.#24034
  • The direct sum of a subspace and its orthogonal complement is the whole space.#23922
  • The echelon form can be used to determine if a linear system is consistent.#23771
  • The eigenspace of a linear transformation is a nontrivial subspace.#24051
  • The eigenvalues of a matrix are the roots/solutions of its characteristic polynomial/equation.#23705
  • The eigenvalues of a polynomial of a matrix are the polynomial of the eigenvalues.#23691
  • The eigenvalues of a power of a matrix are the power the eigenvalues.#23690
  • The eigenvalues of a scalar multiple of a matrix are the scalar multiples of the eigenvalues.#23689
  • The eigenvalues of a triangular matrix are the entries on the main diagonal.#23709
  • The eigenvalues of the inverse of a nonsingular matrix are the reciprocals of the eigenvalues.#23692
  • The geometry of linear systems#23427
  • The Gram-Schmidt process converts a linearly independent set into an orthogonal set.#23936
  • The identity matrix is the identity for matrix multiplication.#23498
  • The image of a linearly independent set under an injective linear transformation is linearly independent.#24032
  • The image of a spanning set spans the range#24037
  • The inner product of a vector with itself is the square of its norm/length.#23910
  • The intersection of subspaces is a subspace#23967
  • The inverse image of a subspace under a linear transformation is a subspace.#24057
  • The inverse of a composition reverses the order#24030
  • The inverse of a linear transformation is a linear transformation#24028
  • The inverse of a matrix can be expressed in terms of its matrix of cofactors.#23721
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