Information in this syllabus is subject to change over the semester.
| name: Henry Scharf (you can call me Henry) | office: ENR2 319 or virtual |
| email: hscharf@math.arizona.edu | office hours: M/W 11:00am–12:30pm or by appt. |
| (please let me know you’re coming) |
| name: Shudong Sun | office hours (virtual only): Tu 2:00–4:00pm; F 10:00–11:00am |
| email: shudongsun@math.arizona.edu | (please let Shudong know you’re coming) |
| course number: MATH/STAT 571B | location: Bio Sci West 219 |
| semester: Spring 2024 | meeting times: M/W 9:30–10:45am |
| mode of delivery: lecture/lab | platform: D2L, Gradescope, TBA (Discord/Slack/Teams…) |
| prerequisites: MATH 571A. Graduate standing. Some previous experience with statistical software such as R is useful. | |
…all experiments are designed experiments. The important issue is whether they are well designed or not. –Douglas Montgomery, Design and Analysis of Experiments (2013)
The primary objective for this course is to provide you, the students, with a strong foundation in the thoughtful design of scientific experiments. Such a foundation is made up of a mixture of mathematical theory, facility with software for implementing statistical models (e.g., R), and tools for sound, practical decision-making for real applications. Especially important is the ability to properly quantify and represent uncertainty surrounding conclusions made using statistical analyses.
Topics include design fundamentals, completely randomized design; randomized complete blocks; Latin square; factorial; nested; incomplete block and fractional replications for 2k, confounding; general mixed factorials; split plot; analysis of variance.
Design and Analysis of Experiments 8th Edition [DAE] by Douglas Montgomery.
Primary text for MATH/STAT 571B.
An Introduction to R [I2R] by Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto & David Lusseau.
Available free online. For those interested, the book also has a companion online course.
R for Data Science by Hadley Wickham and Garrett Grolemund.
A free online text intended for beginning R users written by the Chief Scientist and Director of Learning at RStudio. Especially for those who live in, or are curious about, the tidyverse.
Applied Linear Regression Models or Applied Linear Statistical Models by Kutner, Nachtsheim, Neter, and Li.
A definitive text on linear statistical models, including regression and generalized linear models. Used for MATH/STAT 571A.
In-class Quizzes (15%): A very short quiz will be given during the first 5 minutes of each class meeting. The lowest two scores will be dropped. All quizzes will contribute equally. Students in online sections should complete the quiz by the end of the day they are assigned.
Homework (45%): Homework assignments will be given every 2-3 weeks (see schedule). Each assignment will begin in class and you will typically have 8 more days to finish and submit solutions online via Gradescope.
Exams (20%): There will be two take-home exams.
Project (20%): Groups will be made up of 3-4 students chosen by me. Each group will identify one or more real data sets and perform an analysis using statistical methods from this course (and beyond, if desired).
I try to grade assignments as quickly as I can because I think it is most useful for you to receive feedback as soon as possible. Grades and feedback will be posted on Canvas. If you have a question about grades or notice an inaccuracy, please let me know right away.
Letter Grade: Students earning final grades in the following ranges will receive the corresponding letter grade or higher (square brackets are inclusive, round parentheses are not).
| Percent | Letter | Grade Point |
|---|---|---|
| [90, 100] | A | 4.0 |
| [80, 90) | B | 3.0 |
| [70, 80) | C | 2.0 |
| [60, 70) | D | 1.0 |
| [0, 60) | F | 0 |
Late Policy: Brief extensions will be granted for assignments when a reasonable request if made at least 48 hours before the due date. If no arrangements have been made in advance, a late penalty of 25% of the total assignment grade per day will be assessed.
Academic Honesty: The University adheres to a strict policy prohibiting cheating and plagiarism including but not limited to:
For further details, see the University’s Code of Academic Integrity.
Motivation to participate in this class needs to come primarily from within. Some of the assessment structures provide minimal external nudging intended to help keep you going (e.g., quizzes), but for the most part your success will be a product of your own internal desire to actually learn this stuff. For my part as the instructor, this means I will try to keep topics as immediately relevant for you as possible. I will try to be responsive to your requests throughout the semester. If you find something boring/useless, I’ll try to take it out. If you want me to go into more depth on a particular topic, I’ll try to make time to do that.
For your part as a student, this means you will have to manage your own time carefully and actively try to make assignments/projects relevant for you/your research. When there is an opportunity, find data sets you care about and want to analyze. Focus on methods you want to be able to take with you throughout your career. A fully engaged student will probably find that they are frequently searching online for more information about something we discussed in class. They may find themselves listening to unassigned podcasts and reading blogs or journal articles written by experts. From time to time, they may bump up against a problem to which the collective response of humanity is “We don’t know how to do that…yet.”
I am committed to ensuring each student’s access to all course materials, time and attention from me as the instructor both in and out of class, and fair opportunities to demonstrate mastery of the course content. Please contact me if you require any special assistance or accommodations and I will be happy to make a plan with you.
“At the University of Arizona, we strive to make learning experiences as accessible as possible. If you anticipate or experience barriers based on disability or pregnancy, please contact the Disability Resource Center (520-621-3268, https://drc.arizona.edu/) to establish reasonable accommodations.”
I am committed to ensuring that students in the online sections for this course have access to all materials, are able to engage with me as an instructor and ask questions, and are able to actively participate in the group project. Please make use of virtual office hours, our class discussion forum, and email for communication.
Students in online sections are expected to take quizzes before viewing recorded lectures. The quizzes are much more valuable for learning this way, and it is the only ethical way to earn grades for the quizzes. While I do not anticipate a need for it, I do record time stamps associated with time-of-access for quizzes and recordings and will adjust grades if there is evidence of unethical behavior.
I encourage you to reach out by email anytime you need help or have a question. I endeavor to respond to all emails within 24 hours during the work week. Generally I will not be able to respond during the weekend. For questions that require a longer response than a few sentences, please visit me during office hours or schedule a meeting with me. For questions that can be easily answered through a straightforward search online, you may receive a terse reply inviting you to find the answer on your own. (For example: STUDENT: When are your office hours again? ME: You can figure that out without my help. I believe in you!)
Religious Observances: Please notify me about planned absences for religious observances in advance.
Medical-related Absences: Please contact me if you need to miss class, etc. due to an illness, injury or emergency. For the purposes of addressing university policy, documentation may be requested.
Class rosters are provided to the instructor with students’ legal names. Please let me know if you would prefer an alternate name and/or gender pronoun.
You are expected to attend all in-person class meetings. However, I understand that occasionally timing conflicts may arise for various personal reasons. Whenever possible, please let me know in advance if you will be absent from a class.
Be respectful of your peers and the instructor and comply with the University’s policy on threatening behavior.
The Art of R Programming by Norman Matloff
Fundamentals of Data Visualization by Claus O. Wilke
There are many other useful references online. If you find a particularly good one you think others would appreciate, please let us know!
We respectfully acknowledge the University of Arizona is on the land and territories of Indigenous peoples. Today, Arizona is home to 22 federally recognized tribes, with Tucson being home to the O’odham and the Yaqui. Committed to diversity and inclusion, the University strives to build sustainable relationships with sovereign Native Nations and Indigenous communities through education offerings, partnerships, and community service.
