Stats 110
This syllabus and everything else you need will be posted on the course website: stats110.stanford.edu.
Learning Objectives
The theme of this class is the ubiquity of uncertainty in statistics and in everyday life.
By the end of this class, you should be able to:
- Carry out and interpret a hypothesis test to determine when a signal is real, not just noise.
- Construct and interpret a confidence interval to quantify uncertainty in a statistic.
- Design, implement, and analyze a survey.
- Design, implement, and analyze a randomized experiment.
- Write code in R to perform basic statistical analyses.
Course Staff
|
Professor | Dennis Sun |
| Lectures | Tues, Thurs 10:30 - 11:50 AM in 320-220 | |
| Office Hours |
Tues 2:30 - 4:30 PM in Sequoia 124 and on Discord or by appointment. |
|
|
TA | Sifan Liu |
| Sections | Wed, Fri 10:30 - 11:20 AM in Encina Center 464 | |
| Office Hours | Wed 4 - 5 PM in Sequoia 105 (Girshick Library) | |
|
TA | Apratim Dey |
| Sections | Wed, Fri 1:30 - 2:20 PM in 160-326 | |
| Office Hours | Thurs 5 - 6 PM in Sequoia 105 (Girshick Library) | |
|
CA | Arnav Gangal |
| Office Hours | Mon 3 - 4 PM in Sequoia 105 (Girshick Library) |
Contact Outside Class and Office Hours
We prefer to talk to you in person, during class or office hours! But if you need to reach us outside of these times, there are several options:
- If you have a question about class logistics or course material, please post it on the class Discord so that everyone can benefit from your question.
- If you have a private concern, pleaes e-mail the staff list
stats110-aut2324-staff@lists.stanford.edu
Please use this list instead of our individual e-mail addresses for a timely response. You can expect a response within 1 business day.
Grading
Your final grade in the course will be determined from the following components. Our goal is to motivate you to learn the material well, without creating unnecessary stress.
| Component | Weight |
|---|---|
|
Participation This class is highly interactive, taught through hands-on activities and discussions. You have to be present to fully experience this class. For this reason, we count attendance towards the final grade. We understand that other events may occasionally conflict with class. If you have to miss class, there are ways to make up the attendance grade. Please e-mail the staff list to discuss the best way make up a particular class. Besides attendance, a small part of the participation grade will be reserved for students who volunteer actively in class and answer questions on the class Discord. |
15% |
Case Studies (posted on the Schedule page) A case study is a self-contained investigation of a statistics or data science question. One case study will be assigned after each lecture, due at the beginning of lecture 1 week later. A case study is shorter than a problem set, typically equivalent to 2-3 questions on a problem set. Solutions will be handed out in lecture, and lectures occasionally even build off the case study that is due, so late case studies will not be accepted under any circumstances. However, there will be two optional case studies due Week 10 that will replace your lowest scores. If you cannot make it to lecture, you should give your completed case study to a classmate to turn in. We do not accept submissions via e-mail. |
20% |
|
Instead of paper-and-pencil exams, there will be two short interviews, where you will demonstrate your understanding of statistics concepts to an instructor. We will provide example questions, as well as opportunities to practice interviews during class time. Although interviews might sound intimidating, we have found that interviews give students more chances to succeed, and they are more useful preparation for your future careers! |
20% |
|
To help you achieve the learning objectives, you will collect your own data and analyze it in two projects. For each project, you will submit a report. Then, you will present one of the projects in a poster session during finals week (instead of a final exam). |
45% |
| Total | 100% |
Regrade Policy
If you believe that we have made a mistake in grading, please fill out this form within 1 week of getting the assignment back, and hand your graded assignment to Professor Sun. Note that Professor Sun will regrade your entire assignment, so your grade could go up or down.