Fall 2025 - STAT 850 G100

Linear Models and Applications (4)

Class Number: 7134

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 3 – Dec 2, 2025: Tue, Thu, 2:30–4:20 p.m.
    Burnaby

  • Prerequisites:

    STAT 350 or equivalent.

Description

CALENDAR DESCRIPTION:

A modern approach to normal theory for general linear models including models with random effects and "messy" data. Topics include experimental units, blocking, theory of quadratic forms, linear contrasts, analysis of covariance, heterogeneous variances, factorial treatment structures, means comparisons, missing data, multi-unit designs, pseudoreplication, repeated measures mixed model formulation and estimation and inference.

COURSE DETAILS:

Course Outline:

1.Introduction; scope of linear models.
2.General theory; least squares and Gauss-Markov theorem; normal linear models; quadratic forms.
3.Anova models; design issues; block designs; fractional factorial designs.
4.Model selection; diagnostics; algorithms; selection criteria.
5.Multicollinearity; ridge regression; robust estimation; the bootstrap.
6.Mixed linear models; generalized linear models; nonparametric regression.

Grading

  • Assignments 20%
  • Presentation 20%
  • Written Report 30%
  • Final Project 30%

NOTES:

Above grading is subject to change.

Materials

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.

To learn more about the academic disciplinary process and relevant academic supports, visit: 


RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.