Spring 2024 - ECON 333 D100

Statistical Analysis of Economic Data (4)

Class Number: 2458

Delivery Method: In Person


  • Course Times + Location:

    Jan 8 – Apr 12, 2024: Tue, 2:30–5:20 p.m.

  • Exam Times + Location:

    Apr 19, 2024
    Fri, 12:00–3:00 p.m.

  • Prerequisites:

    ECON 103 with a minimum grade of C- or ECON 113 with a minimum grade of A-; ECON 105 with a minimum grade of C- or ECON 115 with a minimum grade of A-; ECON 233 or BUS (or BUEC) 232 or STAT 270, MATH 157, all with a minimum grade of C-; 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 is an introduction to the use and interpretation of statistical analysis in the context of data typical of economic applications. We will try to put together economic theory, some Math, and statistical theories and tools to quantify Economics models and to test them. Another name for this project is Econometrics.


The focus of this course is on linear regression, by far the most common method for analyzing the relationship between variables, applied to economic data. Emphasis will be placed on both the use and interpretation of this technique and dealing with common problems the econometricians face.

Assignments will be given on a regular basis. They will contain ‘exam-type’ problems (those that you should be solving without software) and ‘practical’ econometric exercises (those that you need special computer program for). Those econometric exercises will require the use of R, a programming language and free software environment for statistical computing and graphics. We will discuss the software at the beginning of the semester, and information will be posted on CANVAS to assist in learning R. You may want to get a head start familiarizing yourself with R before the semester starts – it is an open-source project, and you can download a free copy and install it on your computer: https://www.r-project.org.

Course announcements, syllabus, additional notes, recommended practice problems, and assignments will be posted on CANVAS.


  • Introduction
  • Review of Probability and Statistics
  • Single Linear Regression and its Uses
  • Multiple Linear Regression and its Uses
  • Binary/Dummy Variables
  • Nonlinear Regression
  • Time Series Regression


  • Assignments 25%
  • Midterm Tests 30%
  • Final Exam 45%



James H. Stock, Mark W. Watson, Introduction to Econometrics, 4th edition, Pearson, 2018.


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:

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 during the second month of classes. If your course has a final exam, please ensure that you are available during the entire final exam period 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 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