Fall 2021 - STAT 350 D100

Linear Models in Applied Statistics (3)

Class Number: 5062

Delivery Method: In Person

Overview

  • Course Times + Location:

    Mo 8:30 AM – 9:20 AM
    WMC 3210, Burnaby

    Th 8:30 AM – 10:20 AM
    WMC 3210, Burnaby

  • Exam Times + Location:

    Dec 15, 2021
    8:30 AM – 11:30 AM
    WMC 3260, Burnaby

  • Prerequisites:

    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.
  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, goodness-of-fit tests.
  5. Inference: Interpretation of the parameter estimates. Hypothesis tests, p-values, confidence intervals, prediction and intervals. Inferences for a linear function of the regression coefficients.
  6. General Linear Hypotheses: Additional sum of squares principle. Test for lack of fit based on the pure error sum of squares.
  7. Model selection: Effect of the question of interest on the choice of model, difficulties in model selection due to multicollinearity. Automatic variable selection procedures, warnings, and recommendations.
  8. Introduction to weighted least-squares and generalized linear models.
  9. Selected topics

Grading

  • Assignments 20%
  • Midterm 20%
  • Project 20%
  • Final Exam 40%

NOTES:

Above grading is subject to change.

Materials

RECOMMENDED READING:

Introducation to Linear Regression Analysis, 5th ed. by Montgomery, Peck, Vinning. Pulisher: Wiley

Available online for free through the SFU Library
ISBN: 978-0-470-54281-1

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 csdo@sfu.ca


Tutor Requests:
Students looking for a tutor should visit hhttps://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

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

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

TEACHING AT SFU IN FALL 2021

Teaching at SFU in fall 2021 will involve primarily in-person instruction, with approximately 70 to 80 per cent of classes in person/on campus, with safety plans in place.  Whether your course will be in-person or through remote methods will be clearly identified in the schedule of classes.  You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).

Enrolling in a course acknowledges that you are able to attend in whatever format is required.  You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.

Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112) as early as possible in order to prepare for the fall 2021 term.