Summer 2022 - STAT 302 OL01
Analysis of Experimental and Observational Data (3)
Class Number: 4555
Delivery Method: Distance Education
Course Times + Location:
Exam Times + Location:
Aug 14, 2022
12:00 PM – 3:00 PM
EDB 7618, Burnaby
Prerequisites:One of STAT 201, STAT 203, STAT 205, STAT 270, or BUS 232, with a minimum grade of C-.
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.
Mode of Teaching:
This course will have some videos, all of which are asynchronous. There will be no live lectures.
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.
- Assignments (3) 40%
- Final Exam-In Person - Burnaby Campus 60%
You must pass the final exam to pass the course.
Above grading is subject to change.
STAT2 Modeling with Regression and ANOVA, 2nd ed. by Cannon, Cobb, Hartlaub, et al. Publisher: Macmillan Learning
Book is available through the SFU Bookstore
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 https://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
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TEACHING AT SFU IN SUMMER 2022
Teaching at SFU in summer 2022 will involve primarily in-person instruction. Some courses may be offered through alternative methods (remote, online, blended), and if so, this will be clearly identified in the schedule of classes.
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, online, or blended courses 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 summer 2022 term.