Fall 2019 - STAT 350 D100

Linear Models in Applied Statistics (3)

Class Number: 4571

Delivery Method: In Person

Overview

  • Course Times + Location:

    Tu 11:30 AM – 1:20 PM
    WMC 3210, Burnaby

    Th 11:30 AM – 12:20 PM
    WMC 3210, Burnaby

  • Exam Times + Location:

    Dec 13, 2019
    12:00 PM – 3:00 PM
    SSCC 9002, Burnaby

  • Prerequisites:

    STAT 285, MATH 251, and one of MATH 232 or MATH 240.

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. Special methods for ANOVA models: Linear contrasts. Factor and interaction plots. Multiple comparison procedures.
  9. Introduction to weighted least-squares and generalized linear models.

Grading

  • Assignments 20%
  • Midterm 20%
  • Projects 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 Disabilites:
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 http://www.stat.sfu.ca/teaching/need-a-tutor-.html. We accept no responsibility for the consequences of any actions taken related to tutors.

Registrar Notes:

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

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS