Syllabus

Course Overview

One of the most common assumptions made in Statistics is that observations are independent; however, there are many situations in which the data violate this assumption naturally. In this class, we discuss advanced visualization and modeling approaches for correlated data. Topics will include time series, longitudinal, and spatial data analysis.

Broad, Deep when Needed. The goal of this course is to give you a broad introduction to methods used for correlated data. This will give you the ability to learn other advanced topics not covered in this course. We will go deep into the theory when necessary to fully understand the method. There may be methods that we don’t cover in the depth you desire.

Application with theory. This course emphasizes the application to data but we need to know enough about the model theory to interpret the results. We will get theoretical depth but the end goal is the real life application.

One of the most common assumptions made in Statistics is that observations are independent; however, there are many situations in which the data violate this assumption by design. In this class, we discuss advanced visualization and modeling approaches for when the data are correlated. Topics will include time series analysis, longitudinal data analysis, and spatial data analysis. Applications are drawn from across the disciplines. Prerequisite(s): STAT 155 and MATH 354/STAT 354

As a 400-level MSCS capstone, this course has the following expectations for all students:

Seminars

You will attend and subsequently submit a short reflection for at least 2 MSCS seminars, or other related presentations upon instructor approval. Please see the MSCS Events calendar for upcoming seminars.

Capstone project

This course includes a substantial project, and you must pass the project in order to pass the course. If you are an MSCS major, you are required to complete at least one 400-level course, thus at least one capstone project, in your major before spring of your senior year. You will choose one of these completed projects to later present on MSCS Capstone Days during your senior year (Fall for December graduates and Spring for May graduates).

Learning goals

By the end of this course you should be able to:

  • Implement. Specify and fit an appropriate statistical model for correlated data in R.

  • Compare. Understand the model notation and theory to understand the simplifying assumptions of a variety of models and the potential impact on your conclusions.

  • Apply. Recognize how to creatively apply statistical concepts in new settings.

  • Collaborate. Work productively and effectively in a group setting to solve a problem.

  • Communicate. Clearly explain models and conclusions based on fit models based on a real data set.





Course communication

Meet the instructional team

Brianna Heggeseth (instructor)

About me: Statistician and data scientist
MSCS Department @ Macalester College

My primary research interests lie broadly in the study of statistical models and machine learning algorithms and their application in practice. I have collaborate with colleagues in social psychology, environmental epidemiology, and genetic biology. See https://bcheggeseth.github.io/ for more information.



Stat 452 Preceptor

Kyle Sueflow (he/him)

Class of 2026 | MN Native

Data Science (major)


The preceptor drop-in hours will be listed on this MSCS Events Google Calendar.

R/RStudio Preceptor

Nick Kent (he/him)

Class of 2026 | MN Native

Statistics (major)


The R/RStudio preceptor drop-in hours will be listed on this MSCS Events Google Calendar.

Contacting me

Call me “Brianna”

Students often wonder what to call their professors. I prefer to be called by my first name, Brianna (“Bree-AH-na”). If you prefer to be more formal, I’m fine with Professor Heggeseth (“HEG-eh-seth”) and I use the pronouns she/her/hers.

Please help me make sure that I call you by your preferred name (with correct pronunciation!) and pronouns too!

Email

Please email me (bheggese@macalester.edu) with any personal or academic concerns. I will do my best to respond within 24 hours on weekdays and 48 hours on weekends. For content questions, I encourage you to post in our Slack workspace (see below).

Office Drop-In Hours

Where: OLRI 125 (at the bottom of the interior southern staircase)

When:See my Office Hour Google Calendar for up-to-date office hour times.

Why: Office hours are a great time to talk about this class, career planning, or life in general. This is one of my favorite parts of my job, so please come chat! You should plan to attend my office hours at least once in the semester. If these office hour times don’t work with your schedule, I’m available by appointment; email to set up a time to meet!

Letter of Welcome

Hello, and welcome to a new adventure!

I am excited to start this educational journey with you! You may be feeling quite anxious or really excited or both about this new semester.

I want you to have a positive learning experience in this course so I need you to advocate for yourself and your peers. If something about the course is not working for you, please let me know as soon as possible.

Correlated Data is my jam. My research area is in longitudinal data but the issue of repeated measures come up in time series and spatial data! Some of you have seen spatial analysis and mapping in geography, others have seen time series or panel data in economics, and most of you have not encountered any of these topics! No matter where your motivation comes from, you belong in this class and I’m here to challenge and support you. You each have something to learn and something valuable to contribute to this learning community.

Brianna

Slack Discussion Board

Slack is a commonly used communication tool in industry and is useful to be familiar with, so we’ll be using it as our discussion board.

  • If you’re new to Slack, this video provides a quick overview.
  • First join our STAT 452 workspace here.
  • After joining, you can access our workspace here. (You might want to download the Slack app or bookmark this if you have Slack open in your web broswer.)





Guiding values

Learn by doing

Learn by doing. This course is designed to give you practice applying concepts by modeling real data, comparing different statistical tools, and communicating knowledge gained from that process.

Learning by doing entails getting stuck, making mistakes, asking questions, and getting feedback. These may result in discomfort, but working through that discomfort forms some of your most valuable learning experiences and provide you a foundation to continue your learning well beyond Macalester.

Collaboration is essential

Working effectively in a group setting is an essential life skill that requires practice and demonstrably improves your learning.

Beyond discussing the course content, navigating a group setting involves interpersonal communication, self-reflection, and awareness of social dynamics.

To facilitate group work, it can be useful to discuss roles and responsibilities.

  • Facilitator: ensures the group stays on task and is focused, guides consensus-building
  • Recorder: leads the creation of written communication
  • Presenter: leads oral communication with outside entities
  • Reflector: observes group dynamics, ensures equitable distribution of work and benefits
  • Resource Manager: leads the search for appropriate resources needed for success

If working with someone else enrolled in class would be a barrier to your learning, please let your instructor know as soon as possible (no reason needed!).

Community is key

A sense of community and connectedness can provide a powerful environment for learning: Research shows that learning is maximized when students feel a sense of belonging in the educational environment (e.g., Booker, 2016). A negative climate may create barriers to learning, while a positive climate can energize students’ learning (e.g., Pascarella & Terenzini, cited in How Learning Works, 2012).

For these reasons, I design our in-class group activities to intentionally foster commmunity and connectedness. You can help cultivate our classroom community by being thoughtful about the way you engage with others in class.

Active listening is vital

Research on learning theory and how the brain works has taught us that people learn best in community, when they feel safe, seen, heard, and cared about. Effective listening is a key part of this process. How often do you find yourself coming up with a response and waiting to interject rather than listening to what another person is saying? On the flip side, what do you need to feel heard and understood?

To feel connected in community, we need to practice turn-taking and active listening (fully engaged and trying to understand what someone is saying, rather than just listening to respond). I will ask you to discuss how you want to be listened to throughout the semester.

Reflection is paramount

The content you learn will be cool, but I guarantee that as the field evolves, some part of it will become out-of-date during your careers. What you will need to rely on when you leave Macalester is what I want to ensure you cultivate now: learn how to learn. And the cornerstone of a good learning process is reflection.

Reflection is not just fundamental to learning content–it’s fundamental to learning any sort of intellectual, emotional, or physical skill.

Communication is a superpower

Every time I go to a conference talk on a technical topic, it is striking how quickly laptops or phones come out because of the inability to follow. Academics notoriously struggle to make ideas accessible to others.

I want communication to be very different for you.

Every time you communicate ideas–whether through writing, visuals, or oral presentation–I want you to be a total boss. The end product of strong communication is a better experience for all those who have given you their attention. What’s more, the process of crafting effective communication is invaluable for deepening your own understanding.





Course Materials

There is no required textbook to buy for this course.





Additional Resources (Optional):





How to thrive and what to expect

When taking a new course, figuring out the right cadence of effort throughout the week can be a big adjustment. And most of you are doing this for 4 different courses! Below are some suggestions for what to expect in the course and how to focus your time and attention during and outside of class.

Before class

Pre-class readings/videos: Most class periods will have a required reading from the Correlated Data Course Notes and a video walking through the reading. This preparation will help you review ideas from previous courses or to familiarize yourself with new concepts before seeing them for a second time in class. My goal for these readings is for you to get the most out of class time by being able to more easily follow explanations in class and to engage most fully in class activities.

Suggestion
  • Take Notes: Open a notebook and take notes as you as you read the material.
  • Ask (and answer!) questions in the our Slack workspace.
  • Reflect from in-class time about your learning process or interactions with peers while they are still fresh.
  • Start Early After learning a new topic in class, it is helpful to immediately attempt the related exercises on the upcoming homework assignment.
  • Come to instructor office drop-in hours to chat about the course or anything else! 😃

During class

Class time will be a mix of interactive lecture and stretches of small group work implementing the ideas in R.

During the lecture portion,

  • I ask that phones are put away and computers are closed.
  • Take notes on paper.
  • I will pause frequently to prompt a short exercise or ask questions that you’ll reflect on individually or together.

During the small group work,

  • Open the computer only when necessary.
  • Take notes on paper about concepts even when working on the computer.
  • See your tablemates are your support system.

After class

  • Review material from class (finish the in-class activity and check any solutions that may be available on the course site),
  • make progress on the homework,
  • come to office hours, and
  • reflect on your learning.





Grading and feedback

My philosophy

Grading is thorny issue for many educators because of its known negative effects on learning and motivation. Nonetheless, it is ever-present in the US education system and at Macalester. Because I am required to submit grades for this course, it’s worth me taking a minute to share my philosophy about grading with you.

What excites me about being a teacher is your learning. Learning flourishes in an environment where you find meaning and value in what we’re exploring, feel safe engaging with challenging things, receive useful feedback, and regularly reflect on your learning.

It is important to me to create a course structure and grading system that creates an environment for learning to flourish:

  • Finding meaning and value: I am striving to achieve this by creating space for authentic connection between you, your peers, and myself and by encouraging you to explore a topic that intrigues you for your capstone project.

  • Challenges to deepen learning: The assignments and activities that we will use to learn are meant to be challenging so that you can develop your problem solving skills. You should start the assignments as soon as we’ve covered the material in class and come to office hours.

  • Receiving useful feedback and reflecting regularly: In order to learn maximally, you need to BOTH receive good feedback and to reflect thoughtfully about misconceptions in your learning. I will strive to give useful comments and prompts to spur reflection when I see room for improvement.

Assignments and assessments

Homework

Weekly homework will allow for practice in the implementation and interpretation of the theory and methods. These may be extensions of the in-class activities or making progress on a mini-project. These assignments will be due at class time and you’ll have an opportunity to check in with your classmates before turning it in.

Qualitative feedback will be provided on homework.

  • Thoughtful and complete work will receive a grade of Pass.
  • Work with major errors or issues will recieve a grade of Attempted, or
  • Work that is not turned in by the deadline will recieve a grade of No Submission.

Learning Reflections

Roughly every month in the semester, you will write a reflection in which you think about your learning goals, progress, and next steps.

Qualitative feedback will be provided on reflections.

  • Thoughtful and complete reflections will receive a grade of Pass.
  • Reflections that do not show depth of thought, resemble text written by Gen AI, or are copied from another source will recieve a grade of Attempted, or
  • Reflections that are not completed by the deadline will recieve a grade of No Submission.

Content Conversations

You will meet with the instructor 3 times throughout the semester in pairs to discuss course topics. In advance of these 15-minute conversations, you will be provided a detailed prompt. These conversations provide you an opportunity to demonstrate your understanding, support each other in learning, and receiving immediate feedback or redirection, as needed.

Qualitative feedback will be provided on content conversations.

  • Thoughtful, accurate, engaged, and collaborative conversations will receive a grade of Pass.
  • Conversations with major misunderstandings or lack of participation by all members will recieve a grade of Attempted.
  • Not participating in the conversation will recieve a grade of Not Attempted.

Mini Projects

The best way to learn statistics and feel like a statistician is to work on meaningful data-driven projects. For each of the three sub-disciplines, you will work in groups of 2 to 3 to report on your conclusions based on implementing covered statistical methods to a data set (which will be provided).

The analysis for these projects will be started on some homework assignments. You may choose to share these mini projects as a series of blog posts or use them as a portfolio to demonstrate skills and understanding for future employers.

Each mini project will receive a group grade of High Pass, Pass, Low Pass, or No Submission based on the quality of the statistical methods, the justification of the methods, and the communication of the results. Text that resembles text written by Gen AI will earn at most a Low Pass.

Each individual will receive an individual grade adjusted from the group grade based on the individual Git Commits made by that individual.

Capstone Project & Presentation

You will work on a project towards the end of the semester in groups of 2 to 3. Your group will choose one data type (sub-discipline) and you can use the mini-project as a template for the analysis to be performed on a data set of your choosing. The project will conclude with a final written report and final presentation during the scheduled final exam period.

More information will be provided throughout the semester.

Course grading system

Guidelines for Passing

In order to pass the course, you should:

  • Community: Be present and engaged in class for at least 75% of the class periods.
  • Reflections: Attempt all but 1 reflection. Must attempt the final reflection.
  • Homework: Attempt all but 1 homework assignment.
  • Content Conversations: Attempt all content conversations.
  • Mini Projects: Contribute meaningfully (with Github commits from your account) to all 3 mini projects and receive at least a Low Pass on each.
  • Capstone Project: You, as an individual, contribute with Github commits with your account to a low passing written report and presentation.

Guidelines for an A

In order to earn a final letter grade of A, you should aim to do the following:

  • Community: Be on time, present, and engaged in class for at least 90% of the class periods.
  • Reflections: Pass all reflections.
  • Homework: Attempt all and Pass almost all of the homework assignments.
  • Content Conversations: Pass all content conversations.
  • Mini Projects: Contribute meaningfully (with Github commits from your account) to all 3 mini projects and receive at a High Pass on 2 of the 3.
  • Capstone Project: You, as an individual, clearly contribute with Github commits with your account to a high pass written report and presentation.





Other policies

Late work

Homework assignments will generally be due weekly on Tuesday in class.

Deadlines are social contracts. The purpose of deadlines are so that the instructional team can give useful, meaningful feedback in a timely manner. Everyone will be granted three 3-day extensions to use throughout the semester. If you don’t turn in an assignment within 1 hour of the deadline, you will automatically be using one of your extensions.

If you have used all three of your extensions and need more time to complete an assignment, please email me to set up a meeting as soon as possible. An extension may be granted but there may be penalties in your final grade.

Academic integrity

Academic integrity is the cornerstone of our learning community. Students are expected to be familiar with the college’s standards on academic integrity.

I encourage you to work with your classmates to discuss material and ideas for assignments, but in order for you to receive individualized feedback on your own learning, you must submit your own work for homework assignments. You may not pair program for your homework assignments.

You must write your own code and putting explanations into your own words. Always cite any resources (beyond our course materials) you use to complete any course assignments, including AI (see section below).

Generative AI

Consider: Gen AI is like a calculator.

  • In order to know if the result makes sense, you need to have a strong sense of numerical operations.
  • Calculators aren’t used in elementary school when students are developing that foundational sense of numerical operations.
  • They can be used when students can evaluate the output and understand WHY they get the result they do.



You are developing your foundational understanding of advanced statistical models. Thus, I’d highly recommend you to avoid using Gen AI for this course.

  • You are learning how to learn new statistical methods.
  • The process of learning this provides you invaluable skills and understanding that translate to solving other problems.
  • Rather than use a textbook written for a general audience, I’ve crafted course notes and materials specifically for YOU. Trust me and my materials. I offer supplementary materials for you to gain an alternative point of view, but you’ll notice that they are written for a graduate school level.



Could I ask AI or should I ask my professor / classmate / preceptor?

  • Real intelligence beats artificial intelligence
  • Discussing the material with professor / classmates / preceptor is a form of practicing communication skills.
  • The relationship you develop with your professor provides you a life-long professional connection



In summary, if you use Gen AI in this class, you are cheating yourself out of job skills and connections that will directly benefit you long-term.

  • I don’t recommend using Gen AI for summarizing concepts for this course; this is not a standard undergraduate course. My notes are written for YOU; most publicly available materials are either not detailed enough or for a more advanced audience (PhD level audience).
  • I prohibit you from turning in generated text or code (even after making small adjustments) from Gen AI for homework, reflections, mini projects, and the capstone project. It is a violation against the Academic Integrity policy. If I suspect that you have used Gen AI to generate text or code, I will ask you to meet with me to discuss the assignment.
    • Writing/typing from scratch is the best approach to ensure that you have it in your own words.

If you have any questions about your use of AI tools, please contact me to discuss them.





The environment you deserve

Macalester College values diversity and inclusion. We are committed to a climate of mutual respect, free of discrimination based on race, ethnicity, gender identity, religion, sexual orientation, disability, and other identities, in and out of the classroom. This class strives to be a learning environment that is usable, equitable, inclusive, and welcoming.

To help support these goals, we expect you to follow the MSCS Community Guidelines. These guidelines were created by the MSCS faculty and staff in our ongoing efforts to create a community that is more welcoming, supportive, and inclusive.

Respect: Everyone comes from a different path through life, and it is our moral duty as human beings to listen to each other without judgment and to respect one another. I have no tolerance for discrimination of any kind, in and out of the classroom. If you are seeking campus resources regarding on-going microagressions, the Department of Multicultural Life and the Center for Religious and Spiritual Life are wonderful resources.

Empathy: Everyone has a different life situation. This will impact our personal choices and it can cause tension. Please start with empathy for each other. We all have ongoing struggles and worries and we are all trying to do our best given the circumstances.

Be Curious: We are dealing with higher than normal levels of anxiety and all of us have different ways of coping with that stress. As we navigate interpersonal relationships, start with curiosity. Rather than assuming, ask each other questions. Inspired by Ted Lasso.

Sensitive Topics: Data applications span issues in science, policy, and society. As such, we may sometimes address topics that are sensitive for you. I will try to announce in class if an assignment or activity involves a potentially sensitive topic. If you have reservations about a particular topic, please come talk to me to discuss possible options.

Accommodations: If you need accommodations for any reason, please contact Center for Disability Resources to discuss your needs, and speak with me as soon as possible afterwards so that we can discuss your accommodation plan. If you already have official accommodations, please discuss these with me within the first week of class so that you get off to a great start. Contact me if you have other special circumstances.

Title IX: You deserve a community free from discrimination, sexual harassment, hostility, sexual assault, domestic violence, dating violence, and stalking. If you or anyone you know has experienced harassment or discrimination, know that you are not alone. Macalester provides staff and resources to help you find support. More information is available on the Title IX website.

Please be aware that all Macalester faculty (and preceptors when working) are mandatory reporters, which means that if we become aware of incidents or allegations of sexual misconduct, we are required to share the matter with the Title IX Coordinator. Although I have to make that notification, you control how your case is handled, including whether or not you wish to pursue a formal complaint. If you would like to speak to someone confidentially, contact the Hamre Center (651-696-6275), Chaplain staff (651-696-6298), or other local and national resources listed here.

Tutoring @ Mac: Macalester has many resources to support students academically. For example, the Writing Center (WC) provides one-on-one peer tutoring for writing at any stage of a project or paper; the Science and Quantitative Center (SciQ) supports group tutoring for many math, science, and econ courses; and Academic Coaching provides one-on-one support for study skills, time management, reading, note taking, and effective learning strategies. Seeking help is a sign of strength and maturity, and everyone is encouraged to take advantage of the (free) resources available. Check out the Academic Success page for more information about offered services.

General Health and Well-being: I encourage to make your well-being a priority throughout this semester (and beyond!). Investing time into taking care of yourself will help you engage more fully in your academic experience. Remember that beyond being a student, you are a human being carrying your own experiences, thoughts, emotions, and identities with you. It is important to acknowledge any stress you may be facing, which can be mental, emotional, physical, cultural, financial, etc., and how they can have an impact on you and your academic experience. Remember that you have a body with needs. During class time, eat when you are hungry, drink water, use the restroom, and step away/out if you need space or a break. Please do what is necessary while minimizing the impact on others’ ability to be mentally and emotionally present. Outside of the classroom, sleeping, moving your body, and connecting with others can be strategies to help you be resilient at Macalester (and beyond).

If you are having difficulties maintaining your well-being, you are not alone. Please don’t hesitate to contact me and/or find support from physical and mental health resources here and here.