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Exams not due till Weds. April 7. <\a>
Your grade for the course will be determined by your performance on two midterm exams, a final exam, and an in-class/homework grade. Each of the exams will count 30% and the in-class/homework will count 10%.
Text (optional): The Analysis of Variance, by H. Scheffe.
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Due dates for assignments.
Some References (1) Albert,A. Regression and the Moore-Penrose Pseudoinverse (2) Arnold, Steven F. The Theory of Linear Models and Multivariate Analysis. (3) Graybill,F. Theory and Application of the Linear Model (4) Hicks, C. Fundamental Concepts in the Design of Experiments (5) Hogg,R. and Craig,A. Introduction to Mathematical Statistics (6) Rao,C.R. Linear Statistical Inference (7) A Course in Probability and Statistics, by C. Stone, Duxbury.
Summary of Topics for Linear Statistical Models
Examples of statistical problems covered by the general linear model including regression, ANOVA, and ANCOVA; geometry, projections, and the Moore-Penrose inverse; estimability, the Gauss-Markov theorem, least squares estimators; testing linear hypotheses, distributional results for the likelihood ratio and the Wald statistic, application of results to data sets through canned programs like SAS and through Matlab or other specialized programs; traditional formulas via Cochran's theorem; individual comparisons.