Requirements and Grading


There are two required textbooks for this course:

  • Angrist, J. & J. Pischke. (2015) “Mastering Metrics”. Princeton University Press.
  • James, G., D. Witten, T. Hastie, & R. Tibshirani. (2021) “An Introduction to Statistical Learning with Applications in R”. Springer. (Available online here)

All lecture slides, supplemental readings, course videos, and additional material will be provided on the course website.


I expect all students will attend each class, if they are feeling well. Attendance is the easiest way to learn the different topics that will be covered in class and ask questions. Even though I will not take attendance every class, there will be some classes that attendance will be registered, and it will count towards your participation grade.

If you need to justify an absence due to sickness, please do it through the appropriate channels by contacting Student Emergency Services (SES).


It is very important for students and all the instruction team to behave courteous and professional. During class time, avoid outside distractions, and keep your focus on the lecture (texting/chatting or viewing other websites that are not related to the class is not permitted).

My main goal is to get students to be comfortable actively participating in class. Pairs/group discussions will be encouraged, and cold-calling will also be used to get students engaged.

I have a zero-tolerance policy for racist, sexist, xenophobic, homophobic, or any sort of disrespectful language or behavior towards anyone in this class, including other students, TAs, or the instructor. Any student violating this policy will be referred to the Dean’s office for disciplinary proceedings.


There will be one individual midterm, one final exam, and one final group project in this course. Additionally, there will be seven homework assignments during the semester (only six will be counted), and Just in Time Teaching assignments (JITT) that should be completed before each class:

  1. Homework assignments: 30% (5% each, and you can drop one)
  2. JITT assignments: 10%
  3. Participation: 5%
  4. Midterm: 15%
  5. Final: 20%
  6. Final project: 20%

I will use the cutoffs below to translate your overall course average into a final letter grade. These cutoffs are firm; we do not round course averages. So, for example, an overall course average of 89.99 is a B+, not an A-. I sometimes (but not always) curve grades for each assignment, but not for the final grade. There will be no extra credit, except for a small bonus for completing the course evaluation at the end of the semester.

Grade A A- B+ B B- C+ C C- D F
Cutoff 94% 90% 87% 84% 80% 77% 70% 65% 60% <60%

All assignments and exams are “take-home” in this class, and even though there are no extensions set in place for these, you will be given ample time to complete your assignments even if some unforeseeable, but common, situation comes up. However, if you have an extraordinary situation, please reach out to the instruction team.

I now explain each of these grading components in more detail.

  1. Homework assignments (30%)
  • There will be seven homework assignments in this course, with the following (suggested) submission deadlines:

    • HW1: September 9th, 11:59 PM
    • HW2: September 23rd, 11:59 PM
    • HW3: September 30th, 11:59 PM
    • HW4: October 14th, 11:59 PM
    • HW5: November 4th, 11:59 PM
    • HW6: November 18th, 11:59 PM
    • HW7: November 30th, 11:59 PM
  • Homework assignments have to be completed individually. All students are expected to do their own work, and any sign of plagiarism or copying with another student will be penalized. If there is evidence of misconduct, all students involved will be reported to the corresponding office.

  • Homework assignments will be posted online and submitted through Canvas.

  • You must submit your homework write-up as a PDF, when required, as well as your R Script (or Rmarkdown file). Failure to submit your script will be considered as an incomplete homework. The code you submit with your homework should be fully reproducible (i.e. another person running it on their machine should be able to get the same results as you). See guidelines in our course website for R scripts.

  • Only six homework assignments will be considered for your grade, dropping the assignment with the lowest grade. However, you can only drop an assignment for which you haven’t been penalized for misconduct.

  • There is a 10-point penalty for each day your assignment is late. This penalty is to give everyone the same amount of time to submit their work. If for any reason you have an issue submitting a homework that goes beyond the extension, please reach out to the instruction team as soon as possible (ideally, before the deadline).

  1. Just in Time Teaching Assignments (10%)
  • Mini-assignments to be completed before class that should not take more than 15 minutes to complete. These might include questions related to the readings, watching a short video, or answering some questions related to the topics of this class. Additionally, it will include a knowledge-check for the previous class.

  • The objective of these assignments is for you to think about the topics that will be covered in class, motivate the discussion, and at the same time provide additional feedback for the instructor about where the students stand.

  • The knowledge-check portion of the JITT will be graded for correctness, but you can re-submit as many times as you want.

  • Questions related to next class material will not be graded for their correctness, but just for their completion. That being said, your submission should reflect that you tried to answer the questions appropriately (if not, your submission might not be counted).

  • JITT assignments need to be completed two days before class: Sunday 11:59pm for sections with class on Tuesday, and Tuesday 11:59pm for sections with class on Thursday.

  • Make sure you submit your JITT by the deadline. All submissions made after the deadline will not be counted.

  • All students will get one (1) JITT submission that can be dropped. If for any reason you are not able to complete one of the JITTs, you can still get full credit for your submissions as long as you submit all other JITTs.

  1. Participation (5%)
  • In order to incentivize students to show up and ask questions, attendance and in-class participation will count towards your final grade.

  • During the semester, I will record attendance for at least 5 classes. Students can be absent from one of those classes, without penalty. Any further absences need to be justified through the appropriate channels (i.e. Student Emergency Services).

  • If a student misses more than one class where attendance is recorded, they can make up for it by participating in at least 2 classes of recorded participation. Classes where participation is recorded will be chosen randomly throughout the semester.

  • In order for in-class participation to count, students must either ask a specific question or voluntarily answer a question asked by the instructor.

  1. Midterm Exam (15%)
  • The midterm exam will be an individual evaluation. It is intended to be completed in an “open-book” setting (including “open-internet”), but without outside help from other students/people in general.

  • It will have a take-home format (similar to homework assignments), and students should submit both a write-up and code (see homework submission instructions).

  1. Final Exam (20%)
  • The final exam will be an individual evaluation, and it is cumulative. It is intended to be completed in an “open-book” setting (including “open-internet”), but without outside help from other students/people in general.

  • It will have a take-home format (similar to homework assignments), and students should submit both a write-up and code (see homework submission instructions).

  1. Final Project (20%)
  • The final project for this course is intended to tackle a data analysis problem using some of the tools we have seen in this class (either causal inference or prediction, or both). Students must show a pertinent analysis of a question of their choice, collecting data (see resources on available data on the course website), showing descriptive statistics, and conducting sound analysis to tackle the problem at hand.

  • The final project will be completed in groups of 3 or 4 students. There will be different milestones throughout the semester to make sure you are set up to succeed in terms of the project:

    • Friday Sept 9th: Choose a group of 3 or 4 students (0%)
    • Friday Oct 7th: Pitch your idea for the final project, including data ideas (2%)
    • Friday Nov 11th: Preliminary report (5%)
    • Thursday Dec 8th: Final report (8%)
    • Thursday Dec 8th and Friday Dec 9th: Presentations (5%)
  • Please refer to the guidelines on the course website to see what is expected for each of these milestones.

  • Final presentations will be conducted during a “uniform exam schedule”, so please check the dates for your section and let me know in advance if you have a time conflict. All students must attend the final presentations.

    • Section 1 (Tue 10:00-12:00): Thursday Dec 8th 3.30pm – 6.30pm
    • Section 2 (Tue 12:00-2:00): Thursday Dec 8th 7.00pm – 10.00pm
    • Section 3 (Thu 10:00-12:00): Friday Dec 9th 3.30pm – 6:30pm
    • Section 4 (Thu 12:00-2:00): Friday Dec 9th 7:00pm – 10.00pm

Students should always reference any outside resources that they use for completing any assignment. Any unattributed content will be considered plagiarism, resulting in a failing grade and disciplinary measures.

© Magdalena Bennett - licensed under Creative Commons.