STATS 413
Syllabus
Course description
STATS 413: Applied Regression Analysis is an introduction to regression analysis. At a high-level, the course consists of two parts:
- Linear regression: This part of the course is an in-depth study of linear regression.
- Modern regression methods: This part of the course is a high-level tour of statistical methods for regression and classification. It covers chapters 4–8 in Introduction to Statistical Learning (ISLR).
If we have time at the end of the semester, we may cover some advanced topics (e.g. causal inference, time series etc.).
Prerequisites: You should have a good grasp of linear algebra (at the level of MATH 217) and probability (at the level of STATS 412/425). We shall review relevant concepts as they arise, but this should not be the first time you see them.
Course staff
Grading
Your grade is determined by your overall score:
- 20% problem sets, 40% midterm, 40% final or
- 20% problem sets, 80% final,
whichever is higher. Students who obtain an average of at least 90%, 80%, and 70% will receive grades of at least A-, B-, and C- respectively. We may lower the cutoffs at the end of the semester, but we will not raise them.
Problem sets
- Problem sets are assigned weekly on Thursdays and due at noon ET the following Thursday. If you need an extension on a problem set, you must contact the course staff at least 24 hours before the due date. We do NOT accept late problem sets.
- Some problem sets include a problem that checks for completion of the previous week’s lab. This problem is graded like the other problems in the problem set.
- We suggest you type your solutions with LaTeX or RMarkdown. To help you get started, we shall make the LaTeX markup of the problem sets available. If you are new to LaTeX, we recommend editing LaTeX documents on Overleaf.
- Problems are graded on a scale of 1 to 4: 4 pts = essentially correct, 3 pts = minor mistakes (solution is qualitatively correct), 2 pts = major mistakes (solution is qualitatively incorrect), 1 pt = FUBAR. Your grade on a problem set is the average of your grades on the problems.
- You are encouraged to collaborate on problem sets with classmates, but the final write-up (including any code) must be your own.
Exams
There is a midterm and a final:
- midterm: Wed, Oct 13 in class. The midterm covers the first part of the course.
- final: Wed, Dec 15 from 4–6 PM in 1210 Chemistry Building. The final is cumulative; it covers both parts of the course.
Please keep the preceding dates in mind when making plans for fall and winter break. There are no make-up exams. The exams are closed book and closed notes except for a single (letter-sized) handwritten sheet of notes (front and back).
References and textbooks
There is no textbook for the first part of the course; we will provide lecture notes. The textbook for the second part of the course is Introduction to Statistical Learning (ISLR). Two other good references are
- Hayashi’s Econometrics: the first two chapters are good references (albeit at a more advanced level) for the first part of the course;
- Elements of Statistical Learning: this is the more advanced version of ISLR.
Taking the course online
We strongly suggest you take the course in-person, but the course is set up so that you can take it online if necessary. Most course material is available on the course website, so please check it regularly for updates. You can also
- view lecture recordings on Canvas,
- attend office hours on Zoom (see the course staff list for Zoom links),
- submit assignments on Canvas.
The labs are not recorded, but lab material is available on the course website, so you can do the labs by yourself.
If you must take an exam online, then you must take it during the scheduled exam date and time. We will send you the exam paper at the beginning of the exam time on Slack, and you must send us your answers (on Slack) before the end.
Academic misconduct
The College of LSA prohibits all forms of academic dishonesty and misconduct. Minor infractions usually result in a zero on the assignment and a one letter grade reduction; more serious or repeated infractions will result in a failing grade and additional sanctions imposed by the Office of the Assistant Dean. For more information, including examples of behaviors that are considered academic misconduct and potential sanctions, please see LSA’s Community Standards of Academic Integrity.
Accommodations for students with disabilities
We work with Office of Services for Students with Disabilities to determine appropriate accommodations on an individual basis. Please follow the instructions in their website to request accommodations.