General Linear Models - Background Material
Offered By: statisticsmatt via YouTube
Course Description
Overview
Syllabus
Random Vectors and Random Matrices.
Statistical Distributions: Central & Noncentral t Distributions.
Statistical Distributions: Central & Noncentral Chi square df=1 Distributions.
Statistical Distributions: Derive the F Distribution.
Statistical Distributions: NonCentral F Distribution.
Idempotent Matrices.
Independence of Quadratic Forms.
Independence of Quadratic Forms (another proof).
Distribution of quadratic form n(xbar-mu)Sigma(xbar-mu), where x~MVN(mu,sigma).
Distribution of Quadratic Forms (part 1).
Distribution of Quadratic Forms (part 2).
Distribution of Quadratic Forms (part 3).
(1-a)% Confidence Region for a multivariate mean vector when the data are multivariate normal.
Derivative of a Quadratic Form with respect to a Vector.
Projection Matrices: Introduction.
Perpendicular Projection Matrix.
Mean, Variance, and Covariance of Quadratic Forms.
A Square-Root Matrix.
Inverse of a Partitioned Matrix.
The Spectral Decomposition (Eigendecomposition).
Woodbury Matrix Identity & Sherman-Morrison Formula.
Generalized Inverse Matrix.
Generalized Inverse for a Symmetric Matrix.
Gram-Schmidt Orthonormalization Process: Perpendicular Projection Matrix.
Sum of Perpendicular Projection Matrices.
Taught by
statisticsmatt
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