Syllabus
Table of contents
Note: this syllabus may change in response to changing public health circumstances or university protocols.
Overview
Welcome to STA 712! This course covers generalized linear models (GLMs), which generalize the linear regression models you learned in STA 612 to other types of response variables. We will cover both the theory and applications of generalized linear models, and the computational mathematics needed to fit these models. Throughout the semester you will work with real data from a variety of sources, and we will emphasize reproducibility, checking assumptions, and thoughtful data analysis.
Learning goals: By the end of this class, you will be able to
- Use core GLM theory to investigate models with different response variables
- Fit and assess generalized linear models on data to answer real research questions
- Connect GLMs to topics in the broader statistical landscape (e.g. linear regression, classification, GAMs, neural networks)
- Independently learn and apply new statistical topics
Time: MWF 1:00 – 1:50
Location: Manchester 121
Professor: Ciaran Evans
Office: Manchester 329
Email: evansc@wfu.edu (please allow 24 hours for email responses)
Course materials
Laptops: You will need a laptop for this class, and you will sometimes need it during class. I recommend bringing your laptop each day.
Textbook: Generalized Linear Models with Examples in R, by Dunn and Smyth. This book is very readable and has lots of examples and code. We will supplement the book material with additional theory in class.
Supplementary text: For an intuitive explanation of GLMs, with additional examples and case studies, I recommend Beyond MLR: Beyond Multiple Linear Regression, Roback and Legler. The textbook is available, free, at the link provided.
Software: We will be using the statistical software R, through the interface RStudio for working with data and statistical modeling. You will need to download R and RStudio onto your laptop.
Other resources:
Other helpful resources for statistics and data science:
Resources for R:
StackOverflow (for code help; see Academic Integrity info below)
Getting help
If you have any questions about the course (or statistics in general!), please don’t hesitate to ask! I am available during office hours, by appointment, or via email. If you’re emailing about an issue with R, please include a minimum working example (everything I need to reproduce the issue you encountered).
Keep in mind that debugging software issues can take time, so make sure to start the assignments early in case you run into problems.
Office hours: On Monday 3-4 and Wednesday 11-12, I will have office hours in 15-minute appointment slots. You may sign up (one person per slot) here. Please sign up for only one slot at a time. You may attend these appointments either in-person or virtually. If you plan to attend virtually, please let me know beforehand.
On Wednesday 12 - 12:45 and Thursday 1-2, I will have drop-in office hours (no appointment needed) for anyone who wants to stop by.
Times:
- Monday 3:00pm – 4:00pm
- Wednesday 11:00am – 12:45pm
- Thursday 1:00pm – 2:00pm
Course policies
Communication
While course materials will be posted on the course website, I will send messages and announcements through Canvas. Please make sure your Canvas account is set up so that you receive emails when I send these messages.
Participation and illness
Attendance is important, and you are expected to participate actively in class and group activities and discussions. However, your health, and the health of your peers, is crucial. If you are ill, please do not come to class or office hours. All class materials will be posted online, and I can meet with you one-on-one when you have recovered. If you need office hours when you are ill, I am happy to communicate via email or Zoom. Extensions on coursework may be granted on an individual basis under extenuating circumstances.
Extensions
You have a bank of 5 extension days, which you may use over the course of the semester. You may use either 1 or 2 extension days for a give assignment, take-home exam, or project (making the assignment due either 24 or 48 hours after the original due date). If you plan to use an extension, you must email me before the assignment is due.
Extensions in extenuating circumstances, such as family emergencies, will be handled separately and on an individual basis.
Accessibility
If you require accommodations due to a disability or other learning differences, contact Learning Assistance Center & Disability Services at 336-758-5929 or lacds@wfu.edu as soon as possible to better ensure that such accommodations are implemented in a timely fashion. Please feel free to contact me, and I will be happy to discuss any necessary accommodations. I always like to know how to help my students feel comfortable and successful in our course.
Scent-free zone: The 3rd floor of Manchester is a scent-free zone. Please refrain from wearing perfume, cologne, scented lotion, body spray, and all other scented products if visiting the third floor.
Mental health
All of us benefit from support during times of struggle. You are not alone. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is often helpful.
If you or anyone you know experiences any academic stress, difficult life events, or feelings like anx- iety or depression, we strongly encourage you to seek support. The University Counseling Center is here to help: call 336-758-5273 or visit their website at https://counselingcenter.wfu.edu/.
If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night: Counseling Center: 336-758-5273
If the situation is life threatening, call the police: 911 or 336-758-5911 (campus police)
Academic integrity
I expect and require that students conduct themselves in a manner according to the Wake Forest standard for academic integrity. Cheating or academic dishonesty of any kind will not be tolerated. For other information on these matters, please consult the Code of Conduct. For Academic issues please see the College Judicial System.
Sharing code and resources:
There are many online resources for sharing code, such as StackOverflow. Unless otherwise stated, you are free (and encouraged!) to use these resources for help on labs and assignments. However, you must explicitly cite where you have obtained the code (both code you used directly and code used as an inspiration). Any reused code that is not explicitly cited will be treated as plagiarism.
Unless otherwise stated, you are encouraged to collaborate with other students on homework assignments (not projects or exams). If you do so, please acknowledge your collaborator(s) at the top of your assignment. Failure to acknowledge collaborators may result in a grade of 0. You may not copy code and/or answers directly from another student. If you copy someone else’s work, both parties may receive a grade of 0.
Rather than copying someone else’s work, ask for help. You are not alone in this course!
Professionalism
Please refrain from using your laptop, tablet, and phone for anything other than coursework during class.
Course components
Homework assignments
This course includes regular homework assignments to give you practice with the material and help your learning, and so I can give you feedback on your work. These homework assignments will be graded purely on completion; you will receive credit for an assignment if you have completed all questions, submitted the assignment by the due date, and made a good-faith effort to answer each question using course material.
You are welcomed, and encouraged, to work with each other on homework assignments, but you must turn in your own work. If you copy someone else’s work, both parties may receive a 0 for the assignment grade. If you work with someone else, you must write the name of your collaborator(s) on your homework.
Challenge assignments
In addition to regular homework, I will give several challenge assignments throughout the semester. These require you to independently learn something about a topic related to GLMs, but which is not part of the core course content. The purpose of challenge assignments is to cover cool material which we don’t have time to fully cover in class, and to give you practice with independent learning.
Unlike regular homework, challenge assignments will be graded on a Mastered / Not yet mastered scale. I will judge you to have “Mastered” an assignment if your work is of high enough quality that you could use it to teach the topic to another student. I will give you feedback on your challenge assignments, and you may resubmit each “Not yet mastered” assignment once. You must resubmit your work within one week of receiving feedback.
You are welcomed, and encouraged, to work with each other on challenge assignments, but you must turn in your own work. If you copy someone else’s work, both parties may receive a 0 for the assignment grade. If you work with someone else, you must write the name of your collaborator(s) on your homework.
Exams
There will be two take-home exams during the semester. There is no final exam. Take-home exams will focus more on the theory and methodology of generalized linear models, and will give you a chance to demonstrate what you have learned in the class. Further instructions will be provided on each exam.
Like challenge assignments, each exam question will be graded on a Mastered / Not yet mastered scale. I will give you feedback on your exams, and you may resubmit each “Not yet mastered” exam question once. You must resubmit your work within one week of receiving feedback.
Exams must be completed independently; you may not work with other students.
Projects
Statistics and data science in the real world often involves in-depth analysis of complex datasets to answer one or more high-level research questions, and communicating these results to a wider audience. Projects provide an opportunity to develop these skills, and apply the tools you have learned in class and practiced on homework.
There will be two projects in this course. You will be provided with a dataset, and asked to answer one or more research questions with data visualizations and statistical models. You will submit a written report describing your analysis and conclusions.
Like challenge assignments and exams, projects will be graded on a Mastered / Not yet mastered scale. I will give you feedback on your projects, and you may resubmit each “Not yet mastered” project once. You must resubmit your work within one week of receiving feedback. Further instructions will be provided with each project.
Grading
My goal in this course is to help you learn about generalized linear models, but it isn’t clear that a focus on grades helps students learn; in fact, focusing on grades can detract from the learning process. However, we live in a world where some form of grading is necessary, so I have tried to create a grading system which de-emphasizes grades as much as possible. When assigning grades, I believe that
- Homework should be an opportunity to practice the material. It is ok to make mistakes when practicing, as long as you make an honest effort
- Errors are a good opportunity to learn and revise your work
- Partial credit and weighted averages of scores make the meaning of a grade confusing. Does an 85 in the course mean you know 85% of everything, or everything about 85% of the material?
To that end, in this course
- I will give you feedback on every assignment
- Homework is graded on completeness and effort, not correctness
- All other assignments (challenge, exams, projects) are graded as Mastered / Not yet mastered
- If you haven’t yet mastered something, you get to try again!
Your final grade in the course simply reflects how much of the course content you have mastered. The list below shows what you need to do to receive each grade. Plus and minus grades will be determined by the quality of your in-class participation.
To get a D in the course:
- Receive credit for at least 5 homework assignments
To get a C in the course:
- Receive credit for at least 5 homework assignments
- Master one project
- Master at least 80% of the questions on one exam
To get a B in the course:
- Receive credit for at least 5 homework assignments
- Master one project
- Master at least 80% of the questions on both exams
To get an A in the course:
- Receive credit for at least 5 homework assignments
- Master both projects
- Master at least 80% of the questions on both exams
- Master at least 2 challenge assignments
Each grade bundle is an indivisible unit; all assignments in a bundle must meet expectations in order to earn the associated grade. For example, if you only master one project, the highest grade you can achieve is a B.
Late work
No credit will be given for late assignments (homework, challenge problems, exams, projects), though you may extend the due date by using an extension (see above). If you know you cannot turn in assignment (for instance, if you are ill or there is a family emergency), let me know before the assignment is due, and we will work something out. There will be no grade changes after our last day of class.