Multivariate Statistics: Wishart and Hotelling T²

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Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Explores copulas in multivariate statistics, covering properties, fallacies, and applications in modeling dependence structures.

Covers the multivariate normal distribution, moment-generating function, and combinatorics.

Explores properties of 3x3 matrices with real coefficients and determinant calculation methods.

Explores the properties of invertible matrices, including unique solutions and linear independence.

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.

Covers distributions, derivatives, convergence, and continuity criteria in function spaces.

Covers matrix operations, Fourier transformations, Gaussian models, and signal representations using algebraic methods.