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.