Summer 2021 - STAT 604 G100
Analysis of Experimental and Observational Data (3)
Class Number: 2072
Delivery Method: In Person
Course Times + Location:
May 12 – Aug 9, 2021: Mon, 2:30–3:20 p.m.
May 12 – Aug 9, 2021: Thu, 2:30–4:20 p.m.
Exam Times + Location:
Aug 13, 2021
Fri, 8:30–11:30 a.m.
Prerequisites:Any course in Statistics. Open only to students in departments other than Statistics and Actuarial Science.
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.
STAT Workshop Coordinator: Harsha Perera
- Review: Important concepts from the first course in statistics will be reviewed.
- 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.
- 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.
- 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.
- Other topics may include analysis of covariance, the problem of multiple testing, and/or block designs.
Mode of teaching:
- Lecture: synchonous (recorded)
- Midterms: June 17 (14:30-16:20), July 22 (14:30-16:20)
- Final exam: synchronous (date: TBA)
- Assignments 20%
- Midterm 1 20%
- Midterm 2 20%
- Final Exam 40%
You must pass the final exam to pass the course.
Above grading is subject to change.
MATERIALS + SUPPLIES:
You will need access to high speed internet and a webcam.
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.
STAT2 Modeling with Regression and ANOVA, 2nd ed. by Cannon, Cobb, Hartlaub, et al. Publisher: Macmillan Learning
An e-version of the textbook available at vitalsource.com.
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.
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 SUMMER 2021
Teaching at SFU in summer 2021 will be conducted primarily through remote methods, but we will continue to have in-person experiential activities for a selection of courses. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges 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. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (email@example.com or 778-782-3112).