Syllabus
Table of contents
Note: this syllabus may change in response to changing public health circumstances or university protocols.
Overview
Welcome to STA 711! The focus of this course is statistical inference: estimation, hypothesis testing, and confidence intervals. We will cover techniques for parameter estimation, properties of estimators, and testing hypotheses about unknown parameters. Throughout the course, our work will be motivated by logistic regression, which allows us to model a binomial response.
Logistic regression is an example of a generalized linear model (GLM), and you will continue learning about GLMs next semester in STA 712. My intention is to treat STA 711 and STA 712 as a two-course sequence, in which GLMs are used to motivate our study of statistical inference, which we ultimately apply to real models and data.
Time: MWF 3:00 – 3:50
Location: Manchester 122
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: There are two textbooks for this course, which we will also use for STA 712:
Statistical Inference (2nd edition), by Casella and Berger. This is classic textbook for first-year graduate statistics courses, and is an excellent, comprehensive reference. It is also full of good exercises for study and practice.
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 texts: Here are some optional supplementary texts, which you may find useful as additional resources. These are not required for the course.
All of Statistics: A Concise Course in Statistical Inference, by Larry Wasserman. This book is very readable and approachable.
For an intuitive explanation of GLMs, with additional examples and case studies, I recommend Beyond Multiple Linear Regression, by 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.
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 10-11, Wednesday 12:30-1:30, and Thursday 3-4, 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 Thursday 2-3, I will have drop-in office hours (no appointment needed) for anyone who wants to stop by.
Times:
- Monday 10:00am – 11:00am
- Wednesday 12:30pm – 1:30pm
- Thursday 2:00pm – 3:00pm and 3:00pm - 4:00pm
Course policies
Communication
While course materials will be posted on the course website, I will also 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 the Center for Learning, Access, and Student Success at 336-758-5929 or class@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. I will select a few problems on each homework assignment to grade. These problems will be graded on a Mastered / Not yet mastered scale. I will give you feedback on these questions, and you may resubmit each “Not yet mastered” question once. You must resubmit your work within one week of receiving feedback.
To receive credit for an assignment, you must:
- submit the assignment by the due date (or use an extension)
- complete all questions (not just the graded ones)
- make a good-faith effort to answer all questions using course material
- master all the graded questions
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.
Exams
There will be three take-home exams during the semester. The purpose of these exams is to demonstrate your mastery of core course material. Further instructions will be provided on each exam.
Like homework assignments, each exam question will be graded on a Mastered / Not yet mastered scale. For each exam, there will be an optional take-home make-up exam (with different questions). If you do not master all questions on the exam, you may try again on the make-up exam. If you do not master all questions after taking the make-up exam, you may try again on the final exam.
Exams must be completed independently; you may not work with other students.
Final exam
The final exam in this course is optional, and gives you one final opportunity to demonstrate mastery of material that you did not master on the take-home exams. The final will be a 3-hour in-person exam on Friday, May 5 at 2pm.
Grading
My goal in this course is to help you learn statistical inference, but it isn’t clear that a focus on grades helps students learn; in fact, focusing on grades can detract from the learning process. I also believe that learning takes trial and error, and is not a linear 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 and focuses on learning. 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
- Homeworks and exams 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 your work in the course and 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 3 homework assignments
To get a C in the course:
- Receive credit for at least 4 homework assignments
- 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 at least 80% of the questions on two exams
To get an A in the course:
- Receive credit for at least 5 homework assignments
- Master at least 80% of the questions on all three exams
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 receive credit for 4 homework assignments, the highest grade you can achieve is a C.
Late work
No credit will be given for late work (homeworks, exams, make-ups, and resubmissions), 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 the final exam.