Simon Fraser University
Engaging the World

Course Outlines

Spring 2026 - STAT 350 D100

Linear Models in Applied Statistics (3)

Class Number: 4585

Delivery Method: In Person

Overview

  • Course Times + Location:

    Jan 5 – Apr 10, 2026: Tue, 2:30–4:20 p.m.
    Burnaby

    Jan 5 – Apr 10, 2026: Thu, 2:30–3:20 p.m.
    Burnaby

  • Prerequisites:

    STAT 260, STAT 285, MATH 251, and one of MATH 232 or MATH 240, all with a minimum grade of C-.

Description

CALENDAR DESCRIPTION:

Theory and application of linear regression. Normal distribution theory. Hypothesis tests and confidence intervals. Model selection. Model diagnostics. Introduction to weighted least squares and generalized linear models. Quantitative.

COURSE DETAILS:

Outline:

  1. Linear models: Definition, simple and multiple linear regression models, ANOVA models. Incorporating different types of predictor variables and their interactions in the model. Matrix notation. Interpretation of the parameter estimates.
  2. Estimation methods: Least squares, maximum likelihood. Algebraic and geometrical interpretations.
  3. Properties of least squares estimators: Mean, variance, and covariance of least-squares estimators. Expected value of residual sum of squares.
  4. Diagnostic tools: Residual plots, multicollinearity, outliers, influential observations.
  5. Inference: Hypothesis tests, confidence intervals, prediction intervals.
  6. Test for lack of fit based on the pure error sum of squares.
  7. Model selection: Difficulties in model selection due to multicollinearity. Automatic variable selection procedures, warnings, and recommendations.
  8. Introduction to generalized linear models.
  9. Selected topics
This course is part of the University Accreditation Program and meets specific requirements set by the Canadian Institute of Actuaries (CIA). Please consult the CIA website for full details on CIA accreditation.

Grading

  • Quizzes 15%
  • Assignments 10%
  • Midterm 1 20%
  • Midterm 2 20%
  • Final Exam 35%

NOTES:

Above grading is subject to change. You must pass the final exam in order to pass the course.

There will be no make-up midterms.

Materials

RECOMMENDED READING:

Introduction to Linear Regression Analysis, 6th ed. by Montgomery, Peck, Vinning. Publisher: Wiley
ISBN: 978-1-119-57872-7

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.

Department Undergraduate Notes:

Students with Disabilities:
Students requiring accommodations as a result of disability must contact the Centre for Accessible Learning 778-782-3112 or caladmin@sfu.ca.  


Tutor Requests:
Students looking for a tutor should visit https://www.sfu.ca/stat-actsci/all-students/other-resources/tutoring.html. We accept no responsibility for the consequences of any actions taken related to tutors.

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.