Linear Regression: Absence or Presence of Covariates

Related lectures (25)

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Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.

Explores orthogonal projections and the Gram-Schmidt method for constructing bases.

Explores subspaces, spectra, and projections in linear algebra, including symmetric matrices and orthogonal projections.

Delves into singular value decomposition and its applications in linear algebra.

Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Explores linear models, least squares, Gaussian vectors, and model selection methods.

Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.