Spring 2023 - STAT 302 D100
Analysis of Experimental and Observational Data (3)
Class Number: 5865
Delivery Method: In Person
Course Times + Location:
Mo 12:30 PM – 2:20 PM
SSCB 9200, Burnaby
We 12:30 PM – 1:20 PM
SSCB 9200, Burnaby
Exam Times + Location:
Apr 20, 2023
12:00 PM – 3:00 PM
AQ 3181, Burnaby
Prerequisites:One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, or ECON 233, 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 following programs: statistics major, statistics honours, actuarial science major, and actuarial science honours. Students who have taken STAT 350 first may not then take the course for further credit. Quantitative.
STAT Workshop Coordinators: Marie Loughin/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.
- 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:
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
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.
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
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