STA304H1/STA1003H1: Surveys, Sampling and Observational Data
Fall 2021 (Sept-Dec)
Sections L0101 & L0201
Instructor: Prof. Samantha-Jo Caetano
Office hours: Wednesday 11am – 12pm ET and Wednesdays 3pm – 4pm ET (on Zoom)
Preferred pronouns: she/her
Course administrative email
Use the email address for all administrative inquiries, including missed
assessments and re-mark requests. Please note that this email address will not be monitored
after December 31, 2021.
Course web page
All materials will be posted on Quercus https://q.utoronto.ca. Course materials provided
on Quercus are for the use of students currently enrolled in this course only. Distributing
course materials to anyone outside of the course is considered unauthorized use.
Teaching assistants
See the course Quercus page for information about TAs, office hours and contact.
Calendar description
Design of surveys, sources of bias, randomized response surveys. Techniques of sampling;
stratification, clustering, unequal probability selection. Sampling inference, estimates of pop-
ulation mean and variances, ratio estimation. Observational data; correlation vs. causation,
missing data, sources of bias.
Required prerequisites
ECO227Y1 /STA255H1 /STA261H1 /STA248H1 /STA238H1 /STAB57H3 /STA258H5 /
STA260H5 /ECO227Y5
Please note that all prerequisites for all STA courses are strictly enforced and your instructor
cannot waive them. Any questions about prerequisites should be directed to
ug. .
Class format
The course is scheduled for 2 hours per week (per lecture section) to be delivered online
synchronously. We will be using a flipped class for most lessons in this class. Most weeks,
there will be a set of lecture videos available early in the week (posted Monday morning) to
watch prior to the Wednesday synchronous class. The 2 hour time slot (on Wednesdays) will
be held synchronously through Zoom (links are available in our Quercus page). Typically the
2-hour class meeting on Wednesday will be one hour of synchronous lecture, followed by a
second hour scheduled for synchronous, online extra help/office hour.
Suggested Weekly Routine
Monday & Tuesday Wednesday Thursday
Watch weekly asynchronous Attend synchronous Complete weekly quiz.
videos and work on lecture and office Work on homework and/or
assigned homework. hours and extra help. upcoming assessment.
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https://q.utoronto.ca
mailto:ug.
Note: All synchronous lectures will be recorded and made available to all students in the
course.
Accessibility needs
The University of Toronto is committed to accessibility. If you require accommodations for
a disability, or have any accessibility concerns about the course, the classroom, or course
materials, please contact Accessibility Services as soon as possible:
accessibility. or http://www.accessibility.utoronto.ca.
Computing
Computational work is a central part of developing statistical thinking and developing fa-
cility in the use of computational tools for carrying out simulations and data analysis is a
core objective of this course. We will use R, the R Studio IDE, and R Markdown. All of
these are freely available. You need to first install R, and then R Studio. R can be down-
loaded for free from http://cran.r-project.org. R Studio can be downloaded for free from
http://www.rstudio.com/products/rstudio/download/. Additionally, you can also use R
Studio through the U of T Jupyterhub, by selecting the RStudio option and logging in with
your utorID and password, available here: https://jupyter.utoronto.ca
Some resources for using R and R Markdown:
• The course supplementary notes give guidelines on installing and getting started with R
and R Studio.
• A short intro to R workshop is available here:
https://awstringer1.github.io/ssu-r-workshop/ssu-r-workshop.html
• Hands-On Programming with R by Garrett Grolemund, available here:
https://rstudio-education.github.io/hopr
• R for Data Science by Hadley Wickham and Garrett Grolemund, available here:
https://r4ds.had.co.nz
• An R Markdown Cheat Sheet is available at https://rstudio.com/resources/cheatsheets
Reference Materials
We will be relying on material from the following textbooks. Please note, that access to the
textbooks are not mandatory in this course, but having them for reference is recommended.
1. Wu, Changbao and Mary E. Thompson, 2020, Sampling Theory and Practice, Springer.
This is the primary reference for the course.
Available for free as in pdf through the UofT library here.
2. 2. Gelman, Andrew, Jennifer Hill and Aki Vehtari, 2020, Regression and Other Stories,
Cambridge University Press.
3. Kohavi, Ron, Diane Tang, and Ya Xu, 2020, Trustworthy Online Controlled Experiments:
A Practical Guide to A/B Testing, Cambridge University Press.
4. McElreath, Richard, 2020, Statistical Rethinking, 2nd Edition, CRC Press.
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http://www.accessibility.utoronto.ca
http://cran.r-project.org
http://www.rstudio.com/products/rstudio/download/
https://jupyter.utoronto.ca
https://awstringer1.github.io/ssu-r-workshop/ssu-r-workshop.html
https://rstudio-education.github.io/hopr
https://r4ds.had.co.nz
https://rstudio.com/resources/cheatsheets
https://onesearch.library.utoronto.ca/
Practice Problems
There will be weekly quiz problems for practice. Some practice problems may be assigned
from the textbooks, but these will not be graded.
Course Materials
All course materials are copyrighted. If they are from the textbook, the copyright belongs
to the textbook publisher. If they are provided by an instructor (for example, lecture notes,
computer code, assignments, tests, solutions) the copyright belongs to the instructor. Dis-
tributing materials online or sharing them in any way is a copyright violation and, in some
situations, an academic offence.
Communication
Tentatively, we will be using Zoom for most synchronous online meetings in the course. Please
ensure you are able to access your UofT Zoom account as you will need to be signed in this way
to enter any STA304/1003 course related online Zoom meetings. https://utoronto.zoom.us/.
We will be using Piazza as the platform for discussions related to the course material and
assessments. You can find our course page at:
piazza.com/utoronto.ca/fall2021/sta304l0101l0201.
Students can post anonymously to classmates on Piazza, but the identity of the author of all
posts is view able by instructors.
Be sure to read Piazza’s Privacy Policy and Terms of Service carefully. Take time to un-
derstand and be comfortable with what they say. They provide for substantial sharing and
disclosure of your personal information held by Piazza, which affects your privacy. When you
use Piazza, only provide content that you are comfortable sharing under the terms of the
Privacy Policy and Terms of Use. With that being said Piazza will still be considered a part
of our class and thus all posts and conduct on Piazza must remain professional. Posts regard-
ing personal matters such as inquiries about grades, reporting absences, regrade requests,
etc. should be communicated via email (at ) and *NOT* be posted on
Piazza. Piazza is intended for students to receive support regarding course information and
content and thus should be an overall positive and professional environment.
Again, email is appropriate only for personal matters that can not be shared with the rest
of the class. To be fair to all students, we are not able to answer questions about the course
material by email. These questions should be asked on the discussion forum (publicly) or
during office hours.
Inquires about administrative matters, such as missed tests and re-mark requests, should be
sent to . Please note that email will not be monitored on evenings or
weekends (Toronto time), as well as Piazza. Depending on the amount of emails please allow
a reasonable amount of time for email (and Piazza) responses. Please note that this email
address will not be monitored after December 31, 2021.
Announcements and other course information will be posted on Quercus.
Course Content
The course will consider the following overarching themes in statistical theory and data anal-
ysis:
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• Designing a survey or sample that is appropriately gathering information of interest.
• Carrying out a variety of statistical analyses in R to make inference on the data collected
from a survey/sample.
• Identifying and implementing different sampling techniques and different study designs
and the trade-offs involved in each.
• Identifying sources of bias within a study and comment on a study’s design, including
its weaknesses, strengths, and appropriate analyses.
• Clearly communicating results of statistical analyses to technical and non-technical au-
diences.
We will consider various perspectives on these themes, including Bayesian, frequentist, and
likelihood approaches. We will consider methods that rely on mathematical thinking and
methods that rely on computational thinking, with particular emphasis on computational
approaches to analyzing data and understanding statistical methods.
Assessment
Assessment Weight Date
Weekly Quiz Best 6 out of 10 every Thursday (except week of the Test)
3% timed quiz
(0.5% each) due at 11:59pm ET Thursday
Assignment 1 (Individual) 15% Friday October 1 at 11:59pm ET
Assignment 2 (Individual) 15% Friday October 22 at 11:59pm ET
Assignment 3 (Group) 15% Friday November 5 at 11:59pm ET
Test 20% Wednesday November 24 (overlaps with lecture time)
Final Project (Proposal) 1% Friday December 3, 2021 at 11:59pm ET
Final Project (Peer Review) 1% Wednesday December 8, 2021 at 11:59pm ET
Final Project (Final Report) 30% Friday December 17, 2021 at 11:59pm ET
Notes:
• No accommodation for missed weekly quizzes beyond the flexibility already built into
the grading scheme (i.e., best 6 of 10).
• Assignments will be posted on Quercus at least one week in advance of the due date.
• The test will be held to overlap with the Wednesday November 24 lecture period window,
once the test is started you will have a time limit within that window.
• Extensions for assignments may be granted, to a maximum of 3 days. Requests must be
made in advance of the assignment due date via the course email ( ).
Please note that just because a request is made does not mean the request will be granted.
Additionally, if a request is made on the due date it may not be granted. Thus it is
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recommended to make a submission before the due date regardless of the extension being
granted or not. Requests for extensions made after the due date will not be considered.
• Extensions granted on assignments will not exceed 3 days, to avoid overlapping with
upcoming assessments and to ensure grading is completed in a reasonable time frame.
• Extensions for up to 3 days for the final report of the final project may be granted.
Requests must be made in advance of the final report due date via the course email.
Again, just because a request is made, does not mean that the extension will be granted.
Thus it is recommended to make a submission before the due date regardless of the
extension being granted or not. Requests for extensions made after the due date will
not be considered.
• We will NOT accept email submissions for assessments.
• There will be no extensions granted for the Proposal or Peer Review portion of the Final
Project as it will contain a peer-review and other peer-related components which will
require on-time submissions.
• Late submissions (without a granted extension) will receive a mark of 0.
• If the test is missed you must contact me via the course email within 72 hours of the
missed test. For consideration your email must:
– be received within 72 hours of the test date,
– must include ‘STA304/1003 Reporting Test Absence’ in the subject line,
– must include your full name and student number,
– must include a screenshot/photo of your self-declared absence on Acorn, and
– must include the following two sentences:
1. “I affirm that I am experiencing an illness or personal emergency and I under-
stand that to falsely claim so is an offence under the Code of Behaviour on
Academic Matters.”
2. “I understand that an alternative assessment will be arranged at the instructor’s
discretion (including an oral exam and/or a make-up assessment in December,
after the lecture period).”
• If you miss the test and complete the accommodation procedure correctly (described
above), an alternative assessment will be arranged at the instructor’s discretion. Note
that this alternative assessment may have a different format (e.g., oral assessment) and
may be scheduled in December after classes end.
• Mistakes occasionally happen when marking. If you feel there is an issue with the
marking of a test/assignment, you may request that it be re-marked. The course re-mark
policy exists to correct mistakes, and any request should clearly identify the error (for
example, a question that was not marked, or a total incorrectly calculated). Requests to
correct such mistakes must by form which will be available via a Quercus announcement
when the grades are released. For consideration, any re-mark request:
– must not be sent within the first 24 hours of the release of the assessment grade,
– must be received within one week of the date that the marks for the assessment
became available,
– must include your full name and student number, (and group number if applicable),
and
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– must give a specific, clear, and concise reason for each request, referring to a possible
error or omission by the marker. Re-mark requests without a specific reason will
not be accepted.
Please note that your entire test/assignment may be re-marked when submitting a re-
marking request. Keep in mind that it is possible for your assessment grade to go down
if the regraded mark is lower than your original assessment grade.
For the final project, the re-mark process is handled by the Department of Statistical
Sciences.
Academic integrity
Academic integrity is fundamental to learning and scholarship at the University of Toronto.
Participating honestly, respectfully, responsibly, and fairly in this academic community en-
sures that the University of Toronto degree that you earn will be valued as a true indication
of your individual academic achievement, and will continue to receive the respect and recog-
nition it deserves. Familiarize yourself with the University of Toronto’s Code of Behaviour
on Academic Matters available at http://academicintegrity.utoronto.ca.
Discussion about lecture materials, textbook concepts and course concepts with your class-
mates and the teaching team is encouraged, but it is expected that you work independently
on all individual assessments. Please note, you may not submit for credit any work that
was completed by another student (or person). This includes, but is not limited to, par-
tially or fully completed code, communication of solutions, and plagiarism. In particular, you
are expected to complete and submit independent work for assignments (that are not group
work), the test, and the final project. Specifically, you are expected to work on individual
work, individually. You may discuss lecture materials and general course concepts, but it is
expected that you work individually through assessments. You may use code provided by
your STA304/1003 instructors without providing a citation. If you use code from any other
source, you must provide the source. To protect yourself from potential academic integrity
offences, do not share your code and written submissions.
Writing Resources
Again, communication and writing will play a role in this course, thus I wanted to emphasize
some of the writing resources that the university has made available to its students:
• https://writing.utoronto.ca/writing-centres/arts-and-science/
• https://www.artsci.utoronto.ca/current/academic-advising-and-support/english-language-learning
COVID-19 & Mental Health Resources
This iteration for STA304/1003 will be running during the COVID-19 pandemic, and will
be completely online. There may be times where extensions for students are needed, and/or
instructors and TAs may take longer than usual to respond to emails and/or marking needs.
It is recommended to please stay active in the course as much as possible (attend lectures,
visit office hours, post on Piazza, etc.) and please notify us of needs for extensions or other
course related content as early as possible.
The Faculty of Arts and Science have put together the following list of Frequently Asked
Questions (FAQs) regarding COVID-19:
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https://www.artsci.utoronto.ca/current/academic-advising-and-support/english-language-learning
https://www.artsci.utoronto.ca/covid19-artsci-student-faqs.
Additionally, learning online can be more challenging than learning in-person. If you need
help regarding mental health, please do not hesitate to find support. Here are some UofT
mental health resources:
• https://prod.virtualagent.utoronto.ca/.
• https://studentlife.utoronto.ca/department/health-wellness/.
• Call Good2Talk. Free, confidential helpline with professional counselling, information
and referrals for mental health, addictions and well-being, 24/7/365 1-866-925-5454
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https://www.artsci.utoronto.ca/covid19-artsci-student-faqs
https://prod.virtualagent.utoronto.ca/