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syl - Problem Areas and Diagnostics(Chapters 10,11...

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STA 6207 Regression Analysis Instructor: Dr. Larry Winner Office: 228 Griffin/Floyd Phone: (352) 273-2995 E-Mail: [email protected] Office Hours: M 10:00-11:00, Tu 8:30-9:30, W 11:00-12:00 Text: Applied Regression Analysis, 2nd. Ed . by Rawlings, Pantula, Dickey Course Description: This course provides a survey of theory and applications in linear regression analysis. A full treatment of the linear regression model is covered, focusing on results from mathematical statistics making use of matrix algebra. Computational methods will be used to analyze datasets based on ``canned routines'' as well as a matrix language . Tentative Topics: Simple Linear Regression (Chapter 1) Brief Introduction to Matrix Algebra (Chapter 2.1-2.8) Multiple Regression in Matrix Terms (Chapter 3) Analysis of Variance and Quadratic Forms (Chapter 4) Case Study (Chapter 5) Model Building: Selection of Independent Variables (Chapter 7) Polynomial Models (Chapter 8) Models with Class Variables (Chapter 9.6-9.7)
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Unformatted text preview: Problem Areas and Diagnostics (Chapters 10,11) Transformations (Chapter 12) Intro to Nonlinear Models (Chapter 15.1-15.3) Logistic Regression (15.5) Random Coefficient Regression Models (Chapter 18.3) Tests and Grading: Exam 1- October 5 – 25% Exam 2- November 9 – 25% Exam 3- December 15 (5:30-7:30PM) – 30% Homework - 20% Notes: Exams will be closed note. I will provide any formulas if necessary No make-up exams will be given. Do not plan on leaving town before Final Exam. Homework will be assigned on approximately a weekly basis and you will typically have 2-3 class periods to complete them. No late assignments will be accepted, and you must submit paper copies, not e-mail. Use e-mail sparingly. It is virtually impossible to answer technical questions via e-mail. E-mail is not a substitute for office hours/lecture. SAS and R code for examples in the text are available on class website....
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