Fall 2021 - STAT 302 OL01
Analysis of Experimental and Observational Data (3)
Class Number: 5150
Delivery Method: In Person
The standard techniques of multiple regression analysis, analysis of variance, and analysis of covariance, and their role in observational and experimental studies. This course may not be used to satisfy the upper division requirements of the Statistics major or honours program. Quantitative.
STAT Workshop Coordinator: Marie Loughin
Outline: The course wil cover most of Chapters 1-5 and Sections 7.1 & 7.2.
- Review: Important concepts from the first course in statistics will be reviewed.
- Simple linear regression (SLR): models summarizing the relationship between two quantitative variables. This includes the estimation and interpretation of model parameters, assessment of the model’s it, inference, and prediction.
- Multiple regression: constructing models in which several explanatory variables combine to help explain the variability in a quantitative response variable. This 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): Use of models to compare means of a quantitative response variable between groups de ined by a categorical explanatory variable (e.g. a treatment variable). Includes model assessment and inference for comparison of means. If time allows, other topics in ANOVA may be included, such as analysis of covariance, tests for homogeneity of variances, the problem of multiple testing, and/or block designs.
- R is the programming language that you will use in the course to complete assignments. R will be accessed (for free) using the Jupyter platform at SFU.
Mode of teaching: Distance Education
- Assignments (3) 30%
- Final Exam 70%
You must pass the final exam to pass the course.
Above grading is subject to change.
MATERIALS + SUPPLIES:
Access to high speed internet and a webcam
STAT2 Modeling with Regression and ANOVA, 2nd ed. by Cannon, Cobb, Hartlaub, et al. Publisher: Macmillan Learning
There may be an e-version of the textbook available, please check the Canvas course for details.
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 firstname.lastname@example.org
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.
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 (email@example.com or 778-782-3112) as early as possible in order to prepare for the fall 2021 term.