ECO220Y5Y: Introduction to Data Analysis
and Applied Econometrics
Summer 2025
Instructor:
Email:
Dr. Nicholas Zammit
n.zammit@utoronto.ca
Office:
Office Hours:
KN3272
Tues
11am – 12pm
Course Page:
• https://q.utoronto.ca/courses/391878/
Course Description:
• An introduction to the use of statistical analysis, including such topics as elementary probability
theory, sampling distributions, tests of hypotheses, estimation; analysis of variance and regression
analysis. Emphasis is placed on applications in economics and business problems.
• Learn how to analyze data and how to correctly interpret and explain results. Use Stata to analyze a
wide variety of data and replicate tables and figures in economics research papers.
Objectives: The course aims to provide students with quantitative skills, which are of value to an economist
in both an academic and vocational setting. The course takes an applied approach with emphasis
on moving towards a basic understanding of multiple regression and its issues. In general the
course seeks to provide students with the ability to:
1. Understand the nature of uncertainty and methods of dealing with it.
2. Approach economics empirically.
3. Analyse data with and interpret output tables from econometrics software (Stata).
4. Identify issues and assumptions underlying quantitative analysis
Prerequisites: (ECO101H5(63%) and ECO102H5(63%)) or ECO100Y5(63%) and MAT133Y5(63%) or
(MAT135H5 and MAT136H5) or MAT135Y5 or MAT137Y5
Exclusions: ECO220Y1 or BIO360H5 or BIO361H5 or (MAT123H1 and MAT124H1) or (PSY201H5 and
PSY202H5) or [1.0 credit from (STA218H5 or MGT218H5) or STA220H5 or STA221H5 or STA256H5 or
STA258H5 or STA260H5]
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Course Outline: I have listed readings corresponding to our lecture notes based on two textbook options
below. The textbooks are either Basic Statistics for Business Economics: Sixth Canadian Edition (2018) by
Douglas Lind, William Marchal, Samuel Wathen and Carol Waite and Business Statistics: Third Canadian
Edition (2018) by N. Sharpe, R. DeVeaux, P. Velleman, and D. Wright. Used copies are around and the
The Economics Study Centre has it. Some additional handouts may be provided on the course website to
aid your learning and supplement readings and lecture notes.
Topics Covered:
• Topic 1 – Intro & Descriptive Statistics
– “Math Review” by Prof. Jennifer Murdock for ECO220Y1Y at UofT St.
Click Here to Download
George Campus.
– Basic Statistics for Business & Economics ⇒ Chapter 1 – ‘What Is Statistics’, Chapter 2 –
‘Describing Data: Frequency Tables, Frequency Distributions and Graphic Presentation’, Chapter
3 – ‘Describing Data: Numerical Measures’
– Business Statistics ⇒ Chapter 4 – ‘Displaying and Describing Categorical Data’, Chapter 5
– ‘Displaying and Describing Quantitative Data’, Chapter 6 – ‘Scatterplots, Association and
Correlation’
• Topic 2 – Probability and Random Variables
– Basic Statistics for Business & Economics ⇒ Chapter 4 – ‘A Survey of Probability Concepts’,
Chapter 5.1 to 5.4 – ‘What is a Probability Distribution’, ‘Random Variables’ & ‘The Mean,
Variance, & Standard Deviation of a Probability Distribution’
– Business Statistics ⇒ Chapter 8 – ‘Randomness and Probability’ and Chapter 9.1, 9.2 9.3
‘Random Variables and Probability Distributions’
• Topic 3 – Probability Distributions
– Basic Statistics for Business & Economics ⇒ Chapter 5.5 to 5.7 – ‘Binomial Probability Distribution’ to ‘Poisson Probability Distribution’, Chapter 6 – ‘Continuous Probability Distributions’,
Chapter 11.2 – ‘The F-Distribution’, Chapter LO14.2 – The Chi-square Distribution on pg. 471
– Business Statistics ⇒ Chapters 9.4 to 9.12 (“Random Variables and Probability Distributions”)
• Topic 4 – Statistical Inference I
– Basic Statistics for Business & Economics ⇒ Chapter 7 – ‘Sampling Methods and the Central
Limit Theorem’, Chapter 9 – ‘One-Sample Tests of Hypothesis’, Chapter 8 – ‘Estimation and
Confidence Intervals’
– Business Statistics ⇒ Chapter 10 – ‘Sampling Distributions’, Chapter 11 – ‘Confidence Intervals
for Proportions’, Chapter 12 – ‘Testing Hypothesis about Proportions’, Chapter 13 – ‘Confidence
Intervals and Hypothesis Tests for Means’
• Topic 5 – Statistical Inference II
– Basic Statistics for Business & Economics ⇒ Chapter 10 – ‘Two-Sample Tests of Hypothesis’,
Chapter 11 – ‘Analysis of Variance’
– Business Statistics ⇒ Chapter 14 – ‘Comparing Two Means’
• Topic 6 – Linear Regression – Estimation & Inference
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– Basic Statistics for Business & Economics ⇒ Chapter 12 – ‘Linear Regression & Correlation’,
Chapter 13.1 to 13.4 – ‘Multiple Regression Analysis’, ‘Evaluating a Multiple Regression Equation’ & ‘Inferences in Multiple Linear Regression’
– Business Statistics ⇒ Chapter 7 – ‘Introduction to Linear Regression’, Chapter 18 – ‘Inference
for Regression’, Chapter 20 – ‘Multiple Regression’
• Topic 7 – Linear Regression – Issues & Extensions
– Basic Statistics for Business & Economics ⇒ Chapter 13.5 – ‘Evaluating the Assumptions of
Multiple Regression’
– Business Statistics ⇒ Chapter 19 – ‘Understanding Regression Residuals’, Chapter 21 – ‘Building
Multiple Regression Models’
Assessment:
• In-Person Midterm Test (30%), Remote Practical Submissions (5% per term), Two Remote Group
Data Projects (7.5% each), In-Person Final Exam (45%)
Important Dates:
Midterm Test . . . . . . . . . . . . . . . . . . . . . Tues July 8th at 6pm
Group Data Project #1 . . . . . 11:59pm on Tues June 17th
Group Data Project #2 . . . . . 11:59pm on Tues Aug 12th
Lectures:
• Lecture hours will be delivered both synchronous and asynchronous. Every week there will be prerecorded (asynchronous) lectures available to cover theory and core concepts from the course.
Students can view these lectures as they see fit but should keep up to date with the material. Every
week there will also be a live Zoom lecture (synchronous). If you are registered for Inperson lectures you can attend your live lecture in-person. Live lectures will cover practice
questions and we will work through them on the board. This will give students the chance to ask
questions regarding the theory they have learned from the asynchronous lecture recordings. While the
delivery of these lectures is synchronous, they will be recorded and posted up to Quercus following the
completion of each live lecture. ALL live lectures will be recorded and posted to Quercus. Students
should be aware that practice questions are often discussed via quizzes on Socrative. Many of these
will only be active during the actual live lecture period as answers to them will be discussed in the
live lecture. Hence, for the ability to participate in these practice quizzes and for the full experience
of live lectures students must attend on Zoom.
Tutorials/Labs:
• There will be weekly practicals that will cover similar material to the lectures but will focus on
practical application. Practicals are synchronous and will be held exactly the same as live lectures
described above. Make sure you keep up to date with lecture recordings and bring the cheat
sheet and a non-programmable calculator to your tutorials. A grade of 5% per term will be assigned
for participation based on completion of practical exercises.
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Tests & Exam:
• There will be one midterm test worth 30% of the final grade and a final exam worth 45% of the final
grade.
• Testing will cover material that has been discussed in the lectures, tutorials or readings assigned.
Questions on the midterm will be very similar to tutorial type questions. All topics will be covered on
the final exam as it is cumulative. Non-programmable calculators will be permitted for tests and you
should remember to bring one as there will not necessarily be any spares available.
• Students CANNOT petition to re-write a test once the test has begun. If you are feeling ill, please
do not start the online or in-class test and seek medical attention immediately and see ‘Procedure for
Missed Term Work and/or Final Exam’ below.
Data Projects:
• A central aim of this course is to prepare students for employment or continuing studies in economics
or business which requires applied data skills. Students should be able to convert appropriate data
found online into a useful format. They should be able to read this data into an econometrics software
package and perform basic statistical operations on it. They should then be able to interpret the
output they generate.
• There will be two data projects where students will be motivated by an interesting article or question to
follow this process, generating and then interpreting their findings. Students should form groups
of no more than 4 people and submit one project per group with ALL students named on the
project. All individuals in a group will receive the same grade. Students can complete the data projects
individually if they choose to. Each of the two projects will be worth 7.5% of the final grade.
• Late projects will be subject to a late penalty of 5% per day (including weekends) of the total marks for
the assignment. Projects submitted five calendar days beyond the due date will be assigned a grade of
zero. Projects handed in AFTER the work has been returned to the class cannot be marked for credit.
Accommodations due to late registration into the course will NOT be approved. Extensions will
only be considered for the project under extenuating circumstances as students have the
option to complete these in groups and over the entire length of the term. I would suggest you manage
the time and complete the project well in advance of the deadline to avoid any issues impacting you
nearer the deadline.
• Group Data Projects are subject to the general policy covering online submissions for term work listed
in the syllabus below (see ‘Online Submissions for Term Work’).
Email & Office Policy:
• I will answer questions regarding the course content or procedures by email but I would prefer to meet
students live to answer any questions or concerns. I cannot guarantee any specific response time for
email.
• I would encourage students visit me live during office hours rather than emailing as it will be easier
to answer some questions in-person.
Academic Integrity: The Code of Behaviour on Academic Matters states that:
• The University and its members have a responsibility to ensure that a climate that might encourage, or
conditions that might enable, cheating, misrepresentation, or unfairness is not tolerated. To this end,
all must acknowledge that seeking credit or other advantages by fraud or misrepresentation, or seeking
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to disadvantage others by disruptive behaviour, is unacceptable, as is any dishonesty or unfairness in
dealing with the work or record of a student.
• It is your responsibility as a student at the University of Toronto to familiarize yourself with, and
adhere to, both the Code of Student Conduct and the Code of Behaviour on Academic Matters. This
means, first and foremost, that you should read them carefully.
• Code of Student Conduct and the Code of Behaviour on Academic Matters are available from the U
of T website.
Religious Accommodations: Information about the University’s Policy on Scheduling of Classes and
Examinations and Other Accommodations for Religious Observances is at:
www.viceprovoststudents.utoronto.ca/student-resources/rights-responsibilities/accommodation-religious
Declaration of Temporary Absence: Students who miss an academic obligation during the term (i.e.,
in-class assessment, quiz, paper or lab report) may use the ACORN Absence Declaration Tool to record an
absence in one or more courses. Students may utilize this option once per term for a single absence period of
up to seven consecutive days. The declaration period must include the day of declaration and may include
past and/or future dates, for a total of up to 7 calendar days.
Re-Grading Term Work: A student who believes that their written term work has been unfairly marked
may ask the person who marked the work for re-evaluation. Students have up to one month from the
date of return of an item of term work to inquire about the mark. If the student is not satisfied with this
re-evaluation, they may appeal to the instructor in charge of the course if the work was not marked by the
instructor (e.g., was marked by a TA). Such re-marking may involve the entire piece of work and may raise
or lower the mark. For more information on policies regarding re-marking of term work, please refer to
Re-marking Pieces of Term Work in the Academic Calendar.
Plagiarism Detection Tool: Normally, students will be required to submit their course essays to the
University’s plagiarism detection tool for a review of textual similarity and detection of possible plagiarism.
In doing so, students will allow their essays to be included as source documents in the tool’s reference
database, where they will be used solely for the purpose of detecting plagiarism. The terms that apply
to the University’s use of this tool are described on the Centre for Teaching Support Innovation web site
(https://uoft.me/pdt-faq).
Missed Final Examinations: Students who cannot complete their final examination due to illness or
other serious causes must file an online petition with the Office of the Registrar within 72 hours of the
missed examination. Late petitions will NOT be considered. Upon approval of a deferred exam request,
a non-refundable fee is required for each examination approved. See the Office of the Registrar Deferred
Examinations for more information.
Accommodations for Students with Disabilities: Students with diverse learning styles and needs are
welcome in this course. In particular, if you have a disability/health consideration that may require accommodations, please feel free to approach me and/or Accessibility Services as soon as possible. Accessibility
staff (located in room 2240, Student Services Hub, Davis Building) are available by appointment to assess
specific needs, provide referrals, and arrange appropriate accommodations. Please call 905-569-4699 or email
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access.utm@utoronto.ca. The sooner you let us know your needs the quicker we can assist you in achieving
your learning goals in this course.
Plagiarism Detection Tool: All students are expected to adhere to the Code of Student Conduct (Code
of Student Conduct [December 13, 2019] — The Office of the Governing Council, Secretariat).
Video Recording and Sharing: This course, including your participation, will be recorded on video and
will be available to students in the course for viewing remotely and after each session. Course videos and
materials belong to your instructor, the University, and/or other sources depending on the specific facts
of each situation and are protected by copyright. Do not download, copy, or share any course or student
materials or videos without the explicit permission of the instructor. For questions about the recording and
use of videos in which you appear, please contact your instructor.
Equity and Academic Rights: The University of Toronto is committed to equity, meaningful inclusion,
and respect for diversity. All members of the learning environment in this course should strive to create
an atmosphere of mutual respect towards one another. As a course instructor, I will neither condone nor
tolerate language or behaviour that undermines the dignity or self-esteem of any individual in this course
and wish to be alerted to any attempt to create an intimidating or hostile environment. It is our collective
responsibility to create a learning space that is inclusive and welcomes discussion. Discrimination and harassment will not be tolerated. If you have any questions, comments, or concerns I encourage you to bring
them to me for us to discuss. You may also contact the UTM Equity, Diversity, and Inclusion Office at
edio.utm@utoronto.ca for assistance.
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