STAT 3340
Regression Analysis
Lec 01 selected
A thorough treatment of the theory and practice of regression analysis. Topics include: fitting general linear models using matrices, optimality of least squares estimators (Gauss-Markov theorem), inferences, simple and partial correlation, analysis of residuals, case-deletion diagnostics, polynomial regression, transformations, use of indicator variables for analysis of variance and covariance problems, model selection, and an introduction to nonlinear least squares. This course makes extensive use of computer packages.
Sections
Lecture
Lec 01
Mon, Wed, Fri 14:35-15:25
From the syllabus
From the Fall 2024 syllabus by Bruce Smith. Open the PDF
Grading
20%Midterm examOctober 15, in class
40%Final examScheduled by Registrar
40%Assignments (6)Roughly every two weeks
Textbooks
- Introduction to Linear Regression Analysis, Montgomery, Peck and Vining (optional)
- Matrix Theory and Linear Algebra, Selinger (optional, free)
Course rules
- Late work
- Late assignments will not be accepted.
- Missed test
- Notify the instructor before the test and submit a Student Declaration of Absence form to write a make-up test.
- Assignments
- Submit each assignment as one PDF to Brightspace, with answers in the given order.
- Independent work
- Assignments must be completed independently; identical submissions receive a zero.
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