Requirements and Grading

REQUIRED MATERIALS

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.

ATTENDANCE EXPECTATIONS

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. However, given the current global pandemic we are living through, attendance will not count towards your final grade.

BEHAVIOR EXPECTATIONS

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.

GRADING AND ASSIGNMENTS

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

  1. Homework assignments: 30%
  2. JITT assignments: 10%
  3. Midterm: 20%
  4. Final: 20%
  5. 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, but it will always be in your favor. For forecasting purposes, assume your grade will not be curved. There will be no extra credit.

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

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

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

    • HW1: September 16th, 11:59 PM
    • HW2: September 30th, 11:59 PM
    • HW3: October 14th, 11:59 PM
    • HW4: November 4th, 11:59 PM
    • HW5: November 18th, 11:59 PM
    • HW6: December 2nd, 11:59 PM
  • Homework assignments have to be completed in groups of 3-4 students. The instruction team will provide the assignment for the different groups, and they will change from assignment to assignment.

  • Even though homework assignments are group assignments, all students are expected to know how each question was answered and understand the process that took you to the submitted solution. This will be very helpful for future assignments and also the individual midterm.

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

  • You must submit your homework write-up as a PDF, 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.

  • Every student will get one (1) 24-hr extension for homework assignments, no questions asked. If you want to use the extension, you will need to fill out this Google form before the assignment’s deadline. If one individual asks for a 24-hr extension, the whole group will get an extension.

  • Considering extensions, you will be penalized with 10 points 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.

  • 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.

  • These assignments 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.

  • When you submit a JITT assignment, you will receive a confirmation email. If you don’t receive a confirmation email, assume that the submission was not successful and submit again.

  • 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. Midterm Exam (20%)
  • Due date: October 22nd, 11:59 PM

  • 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%)
  • Due date: December 7th, 11:59 PM

  • 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%)
  • Due date: December 12th, 11:59 PM

  • The final project will be completed in groups. More information will be provided further into the semester.

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.