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

Instructor

	Instructor	Prof. Dennis Sun
	Lectures	Mon, Wed, Fri 1:30 - 2:50 PM in Shriram 104
	Office Hours	Wed 3 - 5 PM in CoDa E230

Tutorial Leaders

Your tutorial will be at the time of the section in which you are enrolled. Exact tutorial assignments (including TA and room) should have been e-mailed to you by the tutorial leader on Wednesday, September 24.

	Mark Robinson		Rui Sun
	David Lyu		Tracy Wu
	Tim Sudijono		Sanchayan Bhowal
	Josh Kazdan		Rahul Kanekar

Contact Outside Class and Office Hours

We prefer to talk to you in person, in tutorial, or in office hours! But if you need to reach us outside of these times, please post on the Ed Discussion forum.

Grading

Your final grade in the course will be computed as follows: $$\text{Final Grade} = \text{Attendance\%} \times \text{Assignments\%}$$

Attendance

This class is highly interactive, taught through hands-on activities and discussions. You have to be present to fully experience this class. Do not take this class if you are unwilling or unable to come to every lecture and section on time. Your attendance grade is simply the percentage of lectures that you attend on time. If you arrive late, then you will receive 50% attendance credit for that lecture.

We realize that other commitments may occasionally conflict with class. If you miss or are late to class, then it is possible to make up the grade by taking an optional final on Wednesday, December 3 in class. Your grade on this final will replace your participation for the classes you missed (or arrived late to). For example, if you attended 80% of classes and score a 70% on the optional final, then your attendance grade would be $$ \text{Attendance}\% = 80\% + 70\% \times (100\% - 80\%) = 94\%. $$

This final gives you the opportunity to fully make up your attendance grade if you miss class. It is also the only way to make up for missed classes; we do not grant exceptions for absences, and we do not distinguish between excused and unexcused absences.

This final really is optional. If your attendance record is near perfect, then you may skip it.

Assignments

Component	Weight
Tutorials Instead of a traditional section, you will attend a weekly tutorial with just 3 students, providing you with highly individualized instruction. Each tutorial will be led by a graduate student. You will be asked to present your solution to the assignment for that week. The TA may have other activities planned, and you can ask questions. The tutorial grade will depend on preparation and participation, rather than correctness. You need to have put in enough effort to be able to substantively discuss the material with your peers. This format is designed to encourage you to focus on the learning process, build a closer relationship with an instructor, obtain feedback in a smaller group setting, and hone your presentation skills in a casual, supportive environment.	15%
Interviews There will be two short interviews, which will replace the tutorials of October 16-17 and November 13-14, where you will demonstrate your understanding of statistics concepts to an instructor. We will provide sample questions. 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! Tutorials are intended to prepare you for these assessments.	30%
Midterm There will be a midterm exam in class on Friday, October 31.	25%
Projects 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).	30%
*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. Note that Professor Sun will regrade your entire assignment, so your grade could go up or down.

Proctoring Pilot

This course is participating in the proctoring pilot overseen by the Academic Integrity Working Group (AIWG). The purpose of this pilot is to determine the efficacy of proctoring and develop effective practices for proctoring in-person exams at Stanford. To find more details on the pilot or the working group, please visit the AIWG's webpage.

Collaboration and AI Policy

The goal of this class is for you to develop fluency in statistical argumentation and reasoning. Too much collaboration and use of AI will hinder your intellectual development. As a result, there will be some opportunities where collaboration and AI use is explicitly allowed (and even encouraged); otherwise, AI use is a violation of the Honor Code.

We have worked hard to design the grading policy so that you are not at a disadvantage, even if other students are using AI (against course policy). For example, the tutorials are graded on participation and effort, so a student who has thought deeply about a problem that they are unable to solve should earn a higher grade than a student who got the correct answer using AI.

Accommodations

Students who may need an academic accommodation based on the impact of a disability must initiate the request with the Office of Accessible Education (OAE). Professional staff will evaluate the request with required documentation, recommend reasonable accommodations, and prepare an Accommodation Letter for faculty dated in the current quarter in which the request is being made.

Once you have your letter, please upload it to this form. (Please don't email it to the professor or your TA; they will just tell you to e-mail it to this list.)

To help us prepare for your accommodations, please submit your letter by Monday, September 29. In order for us to make accommodations for an exam, we must receive your letter at least 10 days before the exam. According to the OAE, accommodations cannot be given retroactively.

Stats 110