Spring 2023 - STAT 604 G100

Analysis of Experimental and Observational Data (3)

Class Number: 5927

Delivery Method: In Person

Overview

  • Course Times + Location:

    Jan 4 – Apr 11, 2023: Mon, 12:30–2:20 p.m.
    Burnaby

    Jan 4 – Apr 11, 2023: Wed, 12:30–1:20 p.m.
    Burnaby

  • Exam Times + Location:

    Apr 20, 2023
    Thu, 12:00–3:00 p.m.
    Burnaby

  • Prerequisites:

    Any course in Statistics. Open only to students in departments other than Statistics and Actuarial Science.

Description

CALENDAR DESCRIPTION:

The standard techniques of multiple regression analysis, analysis of variance, and analysis of covariance, and their role in experimental research. Students with credit for STAT 302 may not take this course for further credit.

COURSE DETAILS:

STAT Workshop Coordinators: Marie Loughin/Harsha Perera

Outline:

  1. Review: Important concepts from the first course in statistics will be reviewed.
  2. Simple linear regression: models summarizing the relationship between two quantitative variables. This unit includes the estimation and interpretation of model parameters, assessment of the model’s fit, inference, and prediction.
  3. Multiple regression: models in which several explanatory variables combine to help explain the variability in a quantitative response variable. This unit includes model assessment, comparison of two regression lines, interactions between explanatory variables, and multicollinearity. Additional topics may include identifying unusual points, variable selection, and/or coding categorical predictors.
  4. Analysis of variance (ANOVA): models that allow the comparison of means of a quantitative response variable across groups defined by a categorical explanatory variable. This unit includes model assessment, inference methods for comparison of means, and tests for homogeneity of variances.
  5. Other topics may include analysis of covariance, the problem of multiple testing, and/or block designs.

 

Grading

  • Assignments 20%
  • Midterm 1 20%
  • Midterm 2 20%
  • Final Exam 40%

NOTES:

You must pass the final exam to pass the course.

Above grading is subject to change.

Materials

MATERIALS + SUPPLIES:

We will be using the R programming language, which you can access via Jupyter, an online platform, at https://sfu.syzygy.ca/. Alternatively, you can download R Studio and R statistical software free of charge from https://www.rstudio.com/ and https://cran.r-project.org/, respectively.

REQUIRED READING:

STAT2 Modeling with Regression and ANOVA, 2nd ed. by Cannon, Cobb, Hartlaub, et al. Publisher: Macmillan Learning

A hard copy of the book is available through the SFU Bookstore
An e-version of the book is available through vitalsource.com

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

SFU’s Academic Integrity website 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