1. Introduction and Preliminaries: Examples, objectives, defining and classifying regression models, sampling schemes, modeling the data, overview, preliminaries.
2. Simple linear regression with uncorrelated errors and constant variance: Chapter 1 and 2 (but not sections 2.7 and 2.8), 4.1-4.4, 4.6, (some parts of ch. 15??)
3. The simple linear regression model with matrix form. (selected part of ch. 5)
4. Multiple linear regression (uncorrelated errors and constant variance).
   1. Estimation and inference. (6.1-6.3, 6.6-6.7)
   2. Analysis of variance, general linear tests and additional sums of squares. (2.7, 2.8, 6.5, 7.1-7.3)
5. Residuals, diagnostics, and remedial measures for some violations of assumptions. (Ch.3, 6.8, (12.1-12.4?))
6. More on multiple linear regression. (7.6-7.8, ch. 11)
7. Model building/variable selection (ch. 8)
8. An overview of additional topics (nonlinear models, further diagnostics, etc.) with brief discussion of some as time permits.

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