Course goals & objectives:

Statistics, at its core, is a set of mathematical tools for reasoning about uncertainty. These tools are invaluable for making sense of data and making predictions about the future. The language of statistics allows us to connect our scientific theories to the phenomena we observe in the world.

This course is designed to introduce you to some of the key ideas in the discipline of statistics, and to make you comfortable with tools for applying them to data that you will encounter in your future courses and outside of class in the future.

By the end of the semester, you should be able to:

  1. Understand how the way that data is collected affects what you can learn from it.
  2. Use statistical software to summarize this data numerically and visually.
  3. Build statistical models of the data and understand which models are better and why.
  4. Make predictions about what kind of data you would expect to see in the future.
  5. Ask questions about the data, and make statistical inferences to answer them.
  6. Present these results in a transparent way to others.
  7. Understand the claims that others make from data and be able to critique them.

Course structure:

The course is divided into four learning units. For each unit a set of learning objectives and required and suggested readings, videos, etc. which will be posted on the course website. You are expected to complete the readings and/or watch the videos familiarize yourselves with the learning objectives.

Lectures and the textbook will cover the bulk of the theoretical ideas, and lab sessions will give you practice applying these ideas to real data. These are deeply intertwined, and it’s important to make sure you understand both the theoretical and practical parts of doing statistics.

Your understanding of the theoretical ideas will be assessed with short weekly quizzes, as well as problem sets that will be due at the end of each unit. Your understanding of the practice of statistics will be assessed by your work on the lab assignments.

The primary goal of this class is to teach you how to apply the ideas and tools of statistics to real-world data. For that that reason, the capstone assessment of the class is a Project in which you will apply these skills to an interesting real-world dataset. There will be No Graded Exams.

You will however be asked to complete the Comprehensive Assessment of Outcomes in a first Statistics class (CAOS) twice: At the start of the course and again at the end. Your responses at the beginning of the class will help us to know what to focus on, and your answers at the end of the quarter will help us to revise the class for the next cohort of students. These assessments will be graded for completion and not for correctness; you will receive full credit independent of your score.


CAOS Pre and Post tests 5%
Quizzes 10%
Problem sets 20%
Labs 40%
Project 25%

Attendance and participation

Lecture and lab attendance is mandatory. Lecture slides will be posted after class each day, but reading these slides is only a partial substitute for attendance – they will often be terse and difficult to interpret without having heard the actual lecture. Similarly, lab assignments will be posted at the start of lab, but your TAs will present additional useful information for successfully completing the assignments.

Problem sets:

These will be assigned at the start of each unit, and will be comprised of problems from the textbook. Each assignment will list a set of problems from the book to be turned in for grading, and roughly 6 practice problems. You do not need to turn in the practice problems, and the solutions can be found in the back of the book.

You are welcomed, and encouraged, to work with each other on the problems. But, you must turn in your own work.

Submission instructions: You will turn in your problem sets by submitted them through Google Classroom. You are welcome to write up problem sets in a word processor of your choice (Word, Google Docs, etc.), but we strongly suggest saving the file as a PDF and submitting the PDF. This will ensure that what we read is exactly what you intended to submit. You are also welcome to do the work in a notebook and upload a picture of each notebook page.

All assignments will be time stamped and late work will be penalized based on this time stamp (see late work policy below).


The objective of the labs is to give you hands on experience with data analysis using modern statistical software. The labs will also provide you with tools that you will need to complete the project successfully. We will use a statistical analysis program called RStudio, which is a front end for the R statistical language.

In class your TAs will give a brief overview of the lab and learning goals, and guide you through some of the exercises. You will start working on the lab during the class session, but note that the labs are designed to take more than just the class time, so you will probably need to continue working on them in order to submit labs before the due date (which will be the following lab session).

Submission instructions: Always submit the .Rmd and .HTML files via Google Classroom.


Each week on Monday we will have a short (~15 min) quiz at the beginning of lecture. You will do these in-class on paper and hand them in. Quizzes are designed to give both you and your instructors rapid feedback about your understanding of the theoretical ideas covered the previous week. We understand that sometimes things come up and you will be late or absent. For this reason, your lowest score will be dropped.


The objective of the project is to give you independent applied research experience using real data and statistical methods. You will be allowed to work on the final project either on your own, or with a partner. The goal will be to synthesize what you have learned in the labs over the course of the semester, and to show that you understand which analyses are appropriate and interesting for which kinds of data.

Announcements & Forum (Google Classroom):

I will regularly send announcements through the Google classroom. You should get an email for these by default, so please make sure to check your email daily to make sure that you don not miss them.

Any non-personal questions related to the material covered in class, problem sets, labs, project, etc. should be posted to our Piazza. Before posting a new question please make sure to check if your question has already been answered. The TAs and I will be answering questions in Piazza daily and all students are expected to answer questions as well. Please use informative titles for your posts.

Note that it is more efficient to answer most statistical questions ``in person” so make use of Office Hours.

Students with disabilities:

Students with disabilities who believe they may need accommodations in this class are encouraged to contact the Student Disability Services Office at (773) 702 6000 as soon as possible to better ensure that such accommodations can be made.

Academic integrity:

The University of Chicago is a community dedicated to honest intellectual inquiry. Cheating on quizzes, plagiarism on homework assignments and projects, lying about an illness or absence and other forms of academic dishonesty are a breach of trust with classmates and faculty, and will not be tolerated. Such incidences will result in a 0 grade for all parties involved as well as being reported to the College Dean of Students. Additionally, there may be penalties to your final class grade. Please review the University of Chicago Student Manual.


  • Late work policy for the problem sets and lab reports:
    • next day: lose 30% of total possible points
    • later than next day: lose all points
  • Late work policy for the project: 10% off for each day late.

  • Regrade requests must be made within one week of when the assignment is returned, and must be submitted in writing. These will be honored if points were tallied incorrectly, or if you feel your answer is correct but it was marked wrong. No regrade will be made to alter the number of points deducted for a mistake.