This exam covers the theory and application of the linear model based mostly on the course content of Stat 607, 608 and 705.
Topics include:
- Multivariate normal distribution
- Linear and quadratic forms and related distributions
- Linear model
- estimation
- estimability
- least squares and perpendicular projection
- maximum likelihood
- sampling distribution
- Gauss Markov
- diagnostics and residuals
- inference
- general linear hypotheses
- analysis of variance
- confidence intervals
References
References for basic Statistics and Probability exams
- Ravishanker and Dey, A First Course in Linear Model Theory, Chapters 1,2,3, 5 and 6.
- Graybill, Theory and Application of the Linear Model, Chapters 3 and 4.
- Seber and Lee, Linear Regression Analysis, 2nd Edition, Chapters 1 and 2.