| Instructor: | Davar Khoshnevisan (Contact Information | Office Hours) |
|---|---|
| Time/Place: | MWF 12:55-13:45, JWB 208. |
| Text: | Linear Regression Analysis, G.A.F. Seber and J. Lee, Wiley, New York, Second edition, 2003. |
| Course Description: | This is the first term in a year-long sequence on linear models.
This course assumes a solid knowledge of Calculus (I, II, III),
linear algebra (Math. 2270), probability theory (Math. 5010),
and mathematical statistics (Math. 5080-5090).
Topics:
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| Grading: | Weekly assignments (0% total); two midterms (25% each), and several projects (50% total). The weekly assignments mostly involve theoretical problems. They are neither collected nor graded. Solutions are posted after the due dates. The projects are applied. The midterms are held in the classroom during lecture times, and held on Sept 26th (midterm 1) and Nov 14th. There are no makeup tests, so you need to ensure that you are on campus at those time in order to take them. |
| Lecture notes: | These are my lectures notes, written to myself. Feel free to use them if you
wish, but they are likely to be rough around the edges. So read carefully.
Module 10: Confidence Intervals and Confidence Sets Midterm 2 is based on the material of Modules 7-9 Module 9: Hypothesis Testing Module 8: Assessing Normality Module 7: Linear Models Modules 1-6 form the mathematical basis of this course; the material for Midterm 1 is on these modules Module 6: Gaussian Random Vectors Module 5: Moment Generating Functions and Independence Module 4: Quadratic Forms Module 3: Some Linear Algebra Module 2: Random Vectors Module 1: Introduction |