Spring 2021 - ECON 333 D200

Statistical Analysis of Economic Data (4)

Class Number: 4502

Delivery Method: In Person


  • Course Times + Location:

    Mo 2:30 PM – 5:20 PM

  • Exam Times + Location:

    Apr 24, 2021
    7:00 PM – 10:00 PM

  • Prerequisites:

    ECON 103 or 200; ECON 105 or 205; ECON 233 or BUS (or BUEC) 232 or STAT 270; MATH 157; 60 units. Students with a minimum grade of A- in ECON 233, BUS (or BUEC) 232 or STAT 270 can take ECON 333 after 30 units. Students seeking permission to enroll based on their ECON 233, BUS (or BUEC) 232 or STAT 270 grade must contact the Undergraduate Advisor in Economics.



An introduction to the use and interpretation of statistical analysis in the context of data typical of economic applications. Students with credit for BUEC 333 may not take this course for further credit. Quantitative.


This course will introduce you to the statistical analysis of economic data: econometrics.  We will focus on linear regression, which is by far the most common method for analyzing the relationship between two or more variables.  The main things you will learn in this class are how to apply regression methods to economic data and how to interpret the results of econometric analysis.  You will also get some experience using statistical software and establish a foundation for further econometric study.


Important information:

  • Lectures will be delivered remotely and asynchronously through Canvas. During the scheduled class time, there will be a weekly or bi-weekly live quiz, a Q&A session, and office hours. Make sure that you are available during the scheduled class time.
  • There will be regular graded assignments consisting of both exam-type questions and computer exercises. You are expected to work independently on the assignments.  Cheating of any kind will result in penalties based on the full extent of the relevant policy.
  • The computer exercises will get you doing some real econometric analysis using the R software package. R is open source, so you can freely download a copy for your own use here: https://www.r-project.org. I encourage you familiarize yourself with R before the start of the semester. You’ll find a good introduction here: https://www.r-econometrics.com/rbasicsintro/
  • Details regarding lab hours will be announced during the first week of class. The lab hours will also be held remotely. It is your responsibility to make yourself available for the lab hours to get extra help on the computer exercises.
  • There will be a course website on Canvas, where assignments, readings, announcements and etc. will be posted. It is your responsibility to check the course website regularly.
  • I encourage you to prepare for ECON 333 by reviewing material from your introductory statistics course. Concentrate your review on probability distributions (especially sampling distributions) and hypothesis testing.  Appendices B and C of Wooldridge are a good summary of this material, and I strongly encourage you to read them before the semester begins.

Topics: Review of Probability and Statistics, Simple Regression Model, Multiple Regression Analysis (Estimation, Inference, Asymptotics), Heteroskedasticity



  • Live quizzes 30%
  • Assignments 20%
  • Midterm 20%
  • Cumulative final exam 30%



Jeffrey M. Wooldridge, “Introductory Econometrics: A Modern Approach” (7th edition), Cengage Learning, 2019.



Wickham, H. & Grolemund, G. “R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media.


Department Undergraduate Notes:

Please note that, as per Policy T20.01, the course requirements (and grading scheme) outlined here are subject to change up until the end of the first week of classes.

Final exam schedules will be released in February. This will allow students to avoid enrollment conflicts, and will significantly reduce instances of exam hardship. If your course has a final exam, please ensure that you are available during the final exam period of April 14 - 26 until you receive confirmation of your exam dates. 

Students requiring accommodations as a result of a disability must contact the Centre for Accessible Learning (CAL) at 778-782-3112 or caladmin@sfu.ca.


Registrar Notes:


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 spring 2021 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. 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 (caladmin@sfu.ca or 778-782-3112).