A brief review of matrices and vector spaces.
Linear models and prediction
The General Linear Model using vector spaces and matrix notation, geometric
interpretation of least squares, (projections, correlations as cosines, dimensions and degrees of freedom. Gauss Markov Theorem, The normal and related distributions, and various other topics) . Special cases: Analysis of Variance and Covariance.
Normal and asymptotic distribution theory for testing,
estimation, and confidence intervals in the General Linear Model .
Examples and applications.