Statistics and
Data Analysis

Why Should I Care?  

Statistics can be viewed as the art and science of using data to learn something about the world. In this course, we will study the basic methods by which statisticians attempt to extract information from data, emphasizing a multivariate view of statistical analysis. We’ll use a data-centric approach and will first explore what we can learn from graphics and other exploratory methods. After a brief introduction to probability, we’ll explore both modern methods for inference such as randomization tests and bootstrapping as well as traditional methods based on Normal theory. 

Topics 

• Exploring data: Categorical and continuous variables, numerical and graphical summaries. 

• Relationships between variables: Contingency tables, box plots, correlation.

• Simple and multiple linear regression: Least squares, interpretations, model selection. 

• Collecting data: Design of experiments and surveys, sources of bias. 

• Randomness and probability: Fundamental probability rules, random variables, distributions, some key properties of random variables. 

• Sampling distributions: Understanding how sample statistics (e.g., mean or proportion) behave across repeated samples. 

• Statistical inference: Confidence intervals and hypothesis tests for various problems. 

Homework (15%)

Homework assignments are due Fridays at 7pm. All homework should be submitted on Gradescope (see Glow for details). You are encouraged to work with classmates on the homework -- just make sure the write-up you submit is your own (so don't copy any text or chunks of R code directly).

• Late work: Life happens! You get one free late pass, which allows you to submit a homework assignment before class time on Monday (11am). After you’ve used up your late pass, you may submit assignments late (before class time on Monday) for up to 90% of full credit. No assignments will be accepted after class begins on the Monday after the original due date, but I will drop your lowest homework score. 

Check-Your-Understanding Quizzes (10%)

There will be a few short Glow quizzes each week (usually broken up by textbook chapter) to check your understanding of the week's material. They will be available Wednesday at noon through Monday at 11 am. You have 3 tries to improve your score on the quiz. 

• Late work: No late quizzes will be accepted, but your lowest 3 quiz grades will be dropped.

Midterms (4 @ 15% each = 60% total)

We will have four midterm "quizzes". All four will emphasize recent material but will be cumulative over the whole semester. 

• Quiz 1 will be an in-class quiz on Friday 2/23. You may bring one 8.5x11 (one-sided) sheet of notes. A calculator will be provided if one is needed. You may not use any other resources or materials. 

• Quiz 2 will be a take-home quiz due Friday 3/15. You may choose any 24 hour period from 3/8-3/15 to work on the midterm. All formal course materials are allowed; you may not use humans other than Prof. Plantinga, any AI tools, or the broader internet (e.g., Google or StackOverflow). 

• Quiz 3 will be an in-class quiz on Friday 4/19 (same rules as Quiz 1). 

• Quiz 4 will be a take-home quiz due Tuesday 5/14 (same rules as Quiz 2). 

Data Analysis Project (10%)

In a group of 3-4 students, you will apply your newly developed skills on a data set that is important and meaningful to you. You will submit a 6-minute video presenting your project no later than the last week of class. More details will be provided throughout the semester. 

• Late work: Generally, late project assignments will not be accepted. Please reach out at least 24 hours in advance of the deadline if you have concerns about submitting a project assignment on time. 

Attendance and Participation (5%)

Engaging with the class is vital to your learning. There are a couple of formal modes of participation: 

• Completion of in-class activities; you may miss up to 3 activities for free

• Other surveys (e.g., the About You form)

Other forms of participation will look different for different students, but I expect you to be attentive; contribute to class, small group, and/or online discussions; identify questions you have based on lectures and homework; and seek ways to get those questions answered (by peers, TAs, or me). 

Classroom Climate

I welcome individuals of all ages, backgrounds, beliefs, ethnicities, genders, gender identities or expressions, national origins, religious affiliations, sexual orientations, ability - and other visible and non-visible differences. All members of this class are expected to contribute to a respectful, welcoming and inclusive environment for every other member of the class. If you have any concerns about classroom climate, please come to me to share your concern. Also, you deserve to be addressed in the manner that you prefer. If you want to be addressed with a particular name and/or pronoun, please let me know!

Access and Accommodation

I am committed to supporting the learning of all students in my class. If you have already registered with the Office of Accessible Education and have a letter of accommodations, please share that with me early in the semester. If you have or think you have a disability (learning, sensory, physical, chronic health, mental health, or attentional), but you haven't been in contact with OAE yet, please reach out to them at oaestaff@williams.edu -- they are a great resource to ensure access and reasonable accommodations.

Time Management

Following college guidelines, in addition to the 2.5 hours we spend together during our class meeting time, you should expect to spend (on average) 10 hours per week on the academic and creative work related to class. If you find that you are spending considerably more (or considerably less!) time to engage with this course academically, please contact me so that we can determine the best course of action as you approach the materials -- we may be able to identify study strategies or skills that could support your success in this class while also helping manage the amount of time required.

Honor Code

Williams’ Honor Code applies to all assignments in this class. In particular, you may work with peers on the regular homework questions (but write-ups you submit should be your own); you are asked to work alone on the Solo Questions; group projects are collaborative efforts; and exams are solo efforts. 

While emerging AI technologies may be able to solve particular homework problems for you, I strongly recommend that you not use them. The goal of homework is to practice what you’re learning in a low-stakes environment and to get feedback on your understanding of the material. You will not receive these benefits from an assignment that ChatGPT completes on your behalf! 

Title IX

Williams College is committed to providing a safe learning environment for all students that is free of all forms of discrimination and sexual harassment, including sexual assault, domestic violence, dating violence, and stalking. If you (or someone you know) has experienced or experiences any of these incidents, know that you are not alone. Williams has staff members trained to support you in navigating campus life, accessing health and counseling services, providing academic and housing accommodations, helping with legal protective orders, and more. Additional information and resources, including confidential resources, are available at: http://titleix.williams.edu/support-and-accommodation/support-and-assistance- for-students/.

Please be aware that all Williams faculty members are responsible employees. That means that if you tell me about a situation involving sexual harassment, sexual assault, dating violence, domestic violence, or stalking, I must share that information with the Title IX Coordinator. Although I have to make that notification, you will control how your case will be handled, including whether or not you wish to pursue a formal complaint. Our goal is to make sure you are aware of the range of options available to you and have access to the resources you need.

Make (Regular) Time

You will be asked to think, compute, and write in this class. You will run into bugs in your code and practice the skill of troubleshooting. Start your homework early so that you have the opportunity to ask questions in office hours -- and so that concepts have time to percolate!

Learn By Practicing

Research on how people learn shows that trying to remember or work through something is much better for your learning than just re-reading it -- even if you get it wrong at first! Some ways you can incorporate this into your studying:

• Work through examples on your own first: When you read the textbook, try to work the examples by yourself, then compare what you did to the book’s process and solution.

• Test yourself: You can do this with flash cards for key concepts and definitions, practice problems, or even talking about what we did in class with classmates or friends (or a parent/grandparent -- whoever will listen!). Fun fact: even unsuccessfully or incorrectly practicing recalling the material helps your brain strengthen the connections to it! Just make sure you look up the right answers later.

• Try in-class activities, book problems, or other practice materials without referring to your notes first – then go back and check your work.

Read the Book

The textbook is a great resource, containing additional explanations of course concepts and an abundance of exercises. Re-read sections that were challenging for you and work through problems both on your own and with your classmates.

Ask Questions!

You are always welcome (and encouraged!) to ask questions during class. Outside of class, you have a variety of options:

• Attend TA sessions: TAs hold regular sessions where students work together on homework or course content and TAs are available to answer questions.

• Come see me: You can come to my office hours, sign up for an appointment, or stop by and see if I’m available. If the door is open, you are welcome to drop in.

• E-mail me: I reply to emails within 24 hours M-F (usually sooner, and sometimes also on weekends, but this is what you can count on!). 

• Get a tutor: Individual peer tutoring is readily available for Stat 201 (and many other gateway courses) free of charge. Peer tutoring is a resource for you to practice your knowledge of course material and ask questions of students who learned it recently. To make individual appointments or see what tutors are available for drop-in meetings, please visit williams.accudemia.net. Reach out to Nick Hanford (nh8@williams.edu) with any questions.

• Visit the Math and Science Resource Center (MSRC): The MSRC is located in Schow 030B. The MSRC is a free resource where you can collaborate on problem sets and talk through difficult concepts with a peer tutor. Check the schedule at bit.ly/wdropin to see when tutors for your class are available and contact Nick Hanford (nh8@williams.edu) with any questions. 

Instructor: Anna Plantinga (amp9@williams.edu, Wachenheim/NSB 238) 

• Note for communication: I am hard of hearing, and I use lip-reading to supplement my hearing. It will be helpful to me (and the rest of the class) if you speak up a little and make sure I can see your face. Thanks! 

Class Sessions: MWF 11:00-12:15 PM (room TBD) 

• We will typically have regular classes MW. I will be in the classroom for HW collaboration/office hours during this time period on Fridays. 

• Exceptions: The two in-class quizzes are scheduled for Fridays. The Wednesday class period prior to each midterm will be an optional Q&A session. 

Office Hours: M 1-2pm, Tu 10-11:30 am, Th 2:30-4 pm, F 1:30-3 pm 

TA Sessions: TBD 

Textbook: Stats: Data & Models, 5th Edition  by De Veaux, Velleman, and Bock. A copy is on 4-hour course reserve in Schow if you prefer not to purchase the book – just plan ahead. 

Course Website: All key course resources will be available on or linked from Glow. In particular, there are links from Glow to Gradescope for homework submission. Note that HW must be in pdf format for Gradescope, and you'll want to leave yourself a couple of extra minutes to select where in the HW each problem appears.

Statistical Software: We will use R and RStudio for all computing in this class. See Glow for installation instructions and more!