Teaching Team

  • Professor: Jesse Gronsbell

  • Teaching Assistant: Junhao Zhu, PhD student in Statistical Sciences


Course Communication

  • Questions about this course should be directed to the discussion board integrated within Quercus, which students can access by logging in with their UTORID. One of the instructors will try to answer promptly. Please see the Course Policies section for more detail.

  • If you don’t think your question is appropriate for the class discussion board (e.g. it is not related to course content or logistics), please reach out to both the professor and the TA using the direct message functionality on Quercus. Allow up to 48 business hours for a reply. Regular emails will not be answered.


Office Hours

  • Teaching Assistant (hybrid): Fridays 10-11, Sidney Smith 621 and Zoom: (Meeting ID: 465 017 9228, Passcode: 2024)

  • Professor: You are welcome to talk to the instructor after class or arrange a time to talk. (Midterm: https://utoronto.zoom.us/j/85964609285)

Class Times and Location


Lectures

  • Tuesday, 10-12pm EST
  • All lecture slides will be posted to the course website once the lecture is finished.


Tutorials

  • Tuesday, 12-1pm EST
  • All content will be posted to the course website once the lab is finished.


Course Description

This course is designed for graduate students in Statistics, Biostatistics, and Computer Science

Review of probability theory, convergence of random variables, statistical models, statistical functionals, maximum likelihood estimation, and computational methods.


Learning objectives

By the end of this course, you should be able to:

  • Understand basic probability theory.

  • Evaluate convergence of sequences of random variables

  • Assess properties of estimators, primarily when \(n\) is large (ie. asymptotics).

  • Use fundamental estimation and inference procedures (eg. MLE, bootstrap).


Evaluation

Students will be evaluated according to University Assessment and Grading Practices Policy. The table below shows the weight of each assessment.

Assessment Weight
Assignment 1 3%
Assignment 2 3%
Assignment 3 3%
Assignment 4 3%
Assignment 5 3%
Midterm 43%
Final Project 42%

You are allowed one late day to use on a homework assignment at any time throughout the course. You must write “I am using the late day” at the top of page 1.

Course Websites

  • Class slides, notes, and other important information can be found on the course website.

  • Questions and Answers can be posted on the Quercus discussion board.


Textbooks

Required texts:

  • Mathematical Statistics by K. Knight [hard copy]

  • All of Statistics by L. Wasserman [ecopy]

Additional textbooks:

  • Statistical Inference by G. Casella & R.L. Berger [hard cover]

  • Elements of Large Sample Theory by E.L. Lehmann [ecopy]

  • Computer Age Statistical Inference by B. Efron & T. Hastie [ecopy]

  • Statistical Models by A.C. Davison [ecopy]


Computing

The course will use R for computing. I highly recommend the RStudio environment. You will need to install both of these on your computer.

You can download R from CRAN and the free Desktop Version of Rstudio here.

I also strongly recommend using R Markdown to produce documents for your analyses.

There are many online resources for R and Rstudio, including the R those on the R resources section of this website and some recommended resources listed below.

  • If you are new to R, you can take a look at Quick-R.
  • If you are experienced with R, the Cheatsheets are invaluable.
  • You can also check out our computing materials from the summer bootcamp offered by the department.

Course Policies


Missed Work Policy

Valid reasons for missing an assessment include: illness, injury, or other relevant personal issues. Any of the following types of documentation will be accepted to verify a student’s reason for missing an assessment:

  • University of Toronto Verification of Student Illness or Injury form. The form must indicate that the degree of incapacitation on academic functioning is moderate, serious, or severe in order to be considered a valid medical reason for missing.

  • Student Health or Disability Related Certificate.

  • A College Registrar’s Letter.

  • Accessibility Services Letter.

If an assignment due date is missed for a valid reason then your assignment will not be subject to a late penalty.

Other reasons for missing an assignment due date, without documentation, will require prior approval by your instructor. If prior approval is not received and an assessment is not submitted on time then your assessment will be subject to a late penalty (see Late Penalty).


Late Penalty

The late penalty for a missed due date is 20% per day (i.e., 24 hours). For example, if the work is submitted after 5 days (including weekend days and holidays) then you will receive a grade of zero for the missed work.


Marking Concerns

Any requests to have your work remarked must contain a written justification for consideration. Remarking requests should be made within one week of receiving your marked work. Re-evaluation appeals are at the discretion of the instructors. Note that adjustments in marks will be rare and could equally result in a lowering or raising of the mark. If a re-revaluation is completed by the instructors, the student must accept the resulting mark as the new mark, whether it goes up or down or remains the same. When appealing a re-evaluation decision, the student accepts this condition.


Communicating with the teaching team

Questions about course material or organization, such as,

  • Is it appropriate to use this statistical method?
  • How do I get rid of this error message?
  • What is the due date?

should be posted on the Quercus discussion board.

If your communication is private, such as, “I missed the test because I was ill”, then contact your instructor.

Always use the direct messaging functionality on Quercus to ensure that your message reaches out the instructor and/or TA’s. Allow up to 72 business hours for a reply. Regular emails will not be answered.


Academic Integrity

You are responsible for knowing the content of the University of Toronto’s Code of Behaviour on Academic Matters.

As a general rule, we encourage you to discuss course material with each other and ask others for advice. However, it is not permitted to share complete solutions or to directly share code for anything that is to be handed in. When an assignment is required to be completed as a team, you may share solutions and code with other members of your team, but not with another team in the class. For example, “For question 2.1 what R function did you use?” is a fair question; “Please show me your code for question 2.1” is not.

If you have any questions about what is or is not permitted in this course, please do not hesitate to contact the professor and/or teaching assistants via Quercus.


Accessibility Needs

Students with diverse learning styles and needs are welcome in this course. If you have an acute or ongoing disability issue or accommodation need, you should register with Accessibility Services (AS) at the beginning of the academic year by visiting http://www.studentlife.utoronto.ca/as/new-registration. Without registration, you will not be able to verify your situation with your instructors, and instructors will not be advised about your accommodation needs. AS will assess your situation, develop an accommodation plan with you, and support you in requesting accommodation for your course work. Remember that the process of accommodation is private: AS will not share details of your needs or condition with any instructor, and your instructors will not reveal that you are registered with AS


Religious Accomodations

As a student at the University of Toronto, you are part of a diverse community that welcomes and includes students and faculty from a wide range of cultural and religious traditions. For my part, I will make every reasonable effort to avoid scheduling tests, examinations, or other compulsory activities on religious holy days not captured by statutory holidays. Further to University Policy, if you anticipate being absent from class or missing a major course activity (such as a test or in-class assignment) due to a religious observance, please let me know as early in the course as possible, and with sufficient notice (at least two to three weeks), so that we can work together to make alternate arrangements.


Specific Medical Circumstances

If you become ill and it affects your ability to do your academic work, consult me right away. Normally, I will ask you for medical documentation in support of your specific medical circumstances. The University’s Verification of Student Illness or Injury (VOI) form is recommended because it indicates the impact and severity of the illness, while protecting your privacy about the details of the nature of the illness. You can submit a different form (like a letter from a doctor), as long as it is an original document, and it contains the same information as the VOI. For more information, please see http://www.illnessverification.utoronto.ca If you get a concussion, break your hand, or suffer some other acute injury, you should register with Accessibility Services as soon as possible.


Accommodation for Personal Reasons

There may be times when you are unable to complete course work on time due to non-medical reasons. If you have concerns, speak to me or to an advisor in the Registrar’s office; they can help you to decide if you want to request an extension or accommodation. They may be able to provide you with a Registrar’s letter of support to give to your instructors, and importantly, connect you with other resources on campus for help with your situation.


Acknowledgements

The following people have contributed to the design of the course materials and website: Prof. Jesse Gronsbell (2020-2022), Jianhui Gao (2022), and Philip Choi (2023).

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