Fall 2019 - STAT 850 G100

Linear Models and Applications (4)

Class Number: 4607

Delivery Method: In Person


  • Course Times + Location:

    Sep 3 – Dec 2, 2019: Wed, Fri, 11:30 a.m.–1:20 p.m.

  • Prerequisites:

    STAT 350 or equivalent.



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 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.


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


Above grading is subject to change.

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:

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Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html