University of Wisconsin-Madison
Statistics 371: Introductory Applied Statistics for the Life Sciences
Summer 2024

Teachers

Name	Live Zoom Q & A / Office Hours MoTuWeTh (excluding our exam days)	Email (please ask most questions via Zoom)
Instructor:
Gillett, John (Teaching Faculty)	8:00-8:30 a.m.	jgillett@wisc.edu
Teaching Assistant(s):
Krauska, Auden	11:00-11:30 a.m. 2:00-2:30 p.m.	krauska@wisc.edu
Zhou, Xiaobin	Mo 6/17 - Mo 7/15 MoTuWeTh 5:00-5:30 p.m, 8:00-8:30 p.m. Th 7/18 - Fr 7/26 MoTuThFr 5:00-5:30 p.m., 8:00-8:30 p.m. Mo 7/29 - Th 8/1 MoTuWeTh 5:00-6:00 p.m. Mo 8/5 - We 8/7 MoTuWe 5:00-5:30 p.m., 8:00-8:30 p.m.	xzhou457@wisc.edu

Meeting Times and Locations

LEC 371-001:

Asynchronous, except for the Q & A Zoom meetings described above

online

Course Description
Introduction to modern statistical practice for students in the life sciences. Topics include: exploratory data analysis, probability and random variables; one-sample testing and confidence intervals, role of assumptions, sample size determination, two-sample inference; basic ideas in experimental design, analysis of variance, linear regression, goodness-of fit; biological applications.

Learning Outcomes
After completing this course, a successful student should be able to:

• Connect data to the scientific contexts in which they arise
• Explore data by choosing appropriately among a variety of graphical and/or numerical summaries of data, creating or computing the summaries, and using these summaries to identify the key features of data
• Use statistical methods to analyze specific types of data through estimation, inference, and model fitting, and recognize when the help of a professional statistician is necessary
• Use a statistical software package to aid in the analysis of data

Requisites
(MATH 112 and placed out of MATH 113), (MATH 113 and placed out of MATH 112), (MATH 112 and 113), MATH 114, 171, or 211 or 221 or placement in MATH 221. Not open to students with credit for STAT 302 or 324.

Designations and Attributes
General Education: Quantitative Reasoning Part B
Breadth: Natural Science
Level: Intermediate
L&S Credit Type: Counts as LAS credit (L&S)
Repeatable for Credit: No

Credit Information
This course is 3-credits. A credit hour is defined as the learning that takes place in at least 45 hours of learning activities, which include time in lectures or class meetings, in-person or online, laboratories, examinations, presentations, tutorials, preparation, reading, studying, hands-on experiences, and other learning activities; or a demonstration by the student of learning equivalent to that established as the expected product of such a period of study.

Instructional Mode
Online. Each week's Canvas module includes these tasks:

• View pre-recorded lecture videos on new material while completing fill-in-the-blank lecture notes (recommended); or read completed notes.
• Solve two (or sometimes one) formative, repeatable Canvas quizzes that are designed to help students engage the lecture material and practice for homework.
• To address questions (optional), attend live online Q&A Zoom sessions with teacher or TA at any of the five times per class day listed above under "Teachers". Links to these sessions are in the "Zoom" tab of our Canvas page.
• Solve two (or sometimes one) homework assignments.

Regular and Substantive Student-Instructor Interaction
The regular and substantive student-instructor interaction requirement is met through five live Q & A sessions per day and feedback on quizzes, homework, and exams.

Discussion Sections
Instead of discussion sections, teacher and TAs lead five live Q & A Zoom sessions per class day.

Online materials
Canvas is used to post lecture notes and videos, formative quizzes, homework, exams, and a gradebook.

Communication
Both Canvas announcements and the university supplied email classlist will be used to share information. It is imperative that your@wisc.edu email is active and working, and that you check it regularly or have it forwarded to the account that you use regularly.

Textbook

No textbook is required. We'll provide course notes. An optional text, for those who want one, is "An Introduction to Statistical Methods and Data Analysis (Sixth Edition)" by R. Lyman Ott and Michael Longnecker (amazon).

Computing
A computer is required that can run RStudio. We won't study the R programming language as such, but will use it by copying and modifying example R code.

In case of computer trouble:

• Consider posit Cloud / RStudio Cloud, which runs in a browser. (To download your knitted ".html" file, see Downloading Files.)
• UW's InfoLabs offer loaner laptops and desktop computer labs that have RStudio on their Windows machines.

Quizzes
There will be approximately twice-weekly Canvas quizzes to help with engaging and understanding lecture material and to prepare for homework.
The quizzes are repeatable and intended for practice, so most students earn nearly all their points. Please do not regard earning quiz points as indicating mastery of the course material.

Homework
There will be approximately twice-weekly homework assignments. Submission will be online using Canvas. You must write homework solutions yourself. Computer code and output must also be your own.

Exams
There is a midterm exam and a final exam. See details under Grading below.

Grading
Grades are at https://canvas.wisc.edu. These points are available:

Canvas quizzes	48	(best 12 of 14 quizzes worth 4 points each; lowest 2 are dropped)
Homework	48	(best 12 of 14 homeworks worth 4; lowest 2 are dropped)
MidtermExam	150	(online Tu 7/16/24 during a 75-minute period of your choice)
FinalExam	154	(online Th 8/8/24 during 2-hour period of your choice)
Total	400

I will assign grades according to the percentage scale, A = [92,100], AB = [88,92), B = [82,88), BC = [78,82), C = [70,78), D = [60,70), F = [0,60) (92% of points ⇒ A); and according to the percentile scale, A = 75, AB = 65, B = 45, BC = 30, C = 10, D = 5, F = 0 (performing better than 75% of the class ⇒ A). Your grade will be the higher of these two grades.

If you anticipate religious or other conflicts with course requirements, or if you require accommodation due to disability, you must notify me during the first three weeks of class. You may not make up missed course work except in the rare case of a documented, serious problem beyond your control.

Help
The TAs and I are eager to help in class and office hours. Free drop-in tutoring is available in the Statistics Learning Center.

How to Succeed in This Course
The successful student will attend lecture and discussion sections, submit homeworks and quizzes, attend exams well-prepared, and use lecture, discussion, office hours, and the Learning Center to ask questions when things are unclear.

Privacy of Student Records & the Use of Audio Recorded Lectures Statement
Lecture materials and recordings for this course are protected intellectual property at UW-Madison. Students in this course may use the materials and recordings for their personal use related to participation in this class. Students may also take notes solely for their personal use. If a lecture is not already recorded, you are not authorized to record my lectures without my permission unless you are considered by the university to be a qualified student with a disability requiring accommodation. [Regent Policy Document 4-1] Students may not copy or have lecture materials and recordings outside of class, including posting on internet sites or selling to commercial entities. Students are also prohibited from providing or selling their personal notes to anyone else or being paid for taking notes by any person or commercial firm without the instructor's express written permission. Unauthorized use of these copyrighted lecture materials and recordings constitutes copyright infringement and may be addressed under the university's policies, UWS Chapters 14 and 17, governing student academic and non-academic misconduct. View more information about FERPA here: https://registrar.wisc.edu/ferpa-facstaff/

Teaching & Learning Data Transparency Statement
The privacy and security of faculty, staff and students' personal information is a top priority for UW-Madison. The university carefully evaluates and vets all campus-supported digital tools used to support teaching and learning, to help suppot success through learning analytics (https://teachlearn.provost.wisc.edu/learning-analytics/), and to enable proctoring capabilities. View the university's full teaching and learning data transparency statement here: https://teachlearn.provost.wisc.edu/teaching-and-learning-data-transparency-statement/.

Course Evaluations
Students will be provided with an opportunity to evaluate their enrolled courses and their learning experience. Most instructors use 'HelioCampus Assessment and Credentialling (formerly AEFIS)', a digital course evaluation survey tool. In most instances, students receive an official email two weeks prior to the end of the semester, notifying them that anonymous course evaluations are available. Student participation is an integral component of course development, and confidential feedback is important. UW-Madison strongly encourages student participation in course evaluations.

Students' Rules, Rights, & Responsibilities
See: https://guide.wisc.edu/undergraduate/#rulesrightsandresponsibilitiestext

Diversity & Inclusion Statement
Diversity is a source of strength, creativity, and innovation for UW-Madison. We value the contributions of each person and respect the profound ways their identity, culture, background, experience, status, abilities, and opinion enrich the university community. We commit ourselves to the pursuit of excellence in teaching, research, outreach, and diversity as inextricably linked goals. The University of Wisconsin-Madison fulfills its public mission by creating a welcoming and inclusive community for people from every background – people who as students, faculty, and staff serve Wisconsin and the world.

Mental Health and Well-Being Statement
Students often experience stressors that can impact both their academic experience and personal well-being. These may include mental health concerns, substance misuse, sexual or relationship violence, family circumstances, campus climate, financial matters, among others. Students are encouraged to learn about and utilize UW-Madison's mental health services and/or other resources as needed. Visit uhs.wisc.edu or call University Health Services at (608) 265-5600 to learn more.

Statement on the Use of ChatGPT and other AI Language Models
While the Statistics Department recognizes the potential benefits of AI language models, their use in academic work can be problematic. In this course, two rules regarding the use of ChatGPT and other AI language models will be enforced: (1) Passing off AI-generated responses as original student work constitutes plagiarism and is strictly prohibited. Any students found to be engaging in this practice will be cited for academic misconduct. (2) Unless explicitly authorized by the instructor to do so, any form of attribution or citation to AI-generated responses as sources is prohibited.

Academic Integrity
By virtue of enrollment, each student agrees to uphold the high academic standards of the University of WisconsinMadison; academic misconduct is behavior that negatively impacts the integrity of the institution. Cheating, fabrication, plagiarism, unauthorized collaboration, and helping others commit these previously listed acts are examples of misconduct which may result in disciplinary action. Examples of disciplinary sanctions include, but are not limited to, failure on the assignment/course, written reprimand, disciplinary probation, suspension, or expulsion.

Standards of Ethical Conduct in Data Analysis and Data Privacy
The members of the faculty of the Department of Statistics at UW-Madison uphold the highest ethical standards of teaching, data, and research. They expect their students to uphold the same standards of ethical conduct. Standards of ethical conduct in data analysis and data privacy are detailed on the ASA website (https://www.amstat.org/ASA/Your-Career/Ethical-Guidelines-for-Statistical-Practice.aspx), and include:

• Use methodology and data that are relevant and appropriate; without favoritism or prejudice; and in a manner intended to produce valid, interpretable, and reproducible results.
• Be candid about any known or suspected limitations, defects, or biases in the data that may affect the integrity or reliability of the analysis. Obviously, never modify or falsify data.
• Protect the privacy and confidentiality of research subjects and data concerning them, whether obtained from the subjects directly, other persons, or existing records.

Netiquette on Piazza and Online Communication
See https://kb.wisc.edu/50548 for a general netiquette. Specifically:

• Any comment or answers must be on topic, concise, polite, and respectful of others.
• Assume the best intentions of others in the class and be forgiving when you think that the tone of someone's post is offensive. It is easy to misread the tone of someone's written communication. If in doubt, ask an open, honest question about what the person meant so that you can clarify before making assumptions that damage your perception of your colleague.
• Students must not post answers to homework problems.

Complaints
If you have a complaint about a TA or course instructor, you should feel free to discuss the matter directly with the TA or instructor. If the complaint is about the TA and you do not feel comfortable discussing it with him or her, you should discuss it with the course instructor. Complaints about mistakes in grading should be resolved with the instructor in the great majority of cases. If the complaint is about the instructor (other than ordinary grading questions) and you do not feel comfortable discussing it with him or her, contact the Director of Undergraduate Studies, Professor Cecile Ane (cecile.ane@wisc.edu).
If your complaint concerns sexual harassment, please see campus resources listed at: https://compliance.wisc.edu/titleix/resources/. In particular, there are a number of options to speak to someone confidentially.
If you have concerns about climate or bias in this class, or if you wish to report an incident of bias or hate that has occurred in class, you may contact the Chair of the Statistics Department Climate and Diversity Committee, Professor Jessi Cisewski-Kehe (jjkehe@wisc.edu). You may also use the University's bias incident reporting system, which you can reach at: https://osas.wisc.edu/report-an-issue/bias-or-hate-reporting/.

Overlapping Course Time Statement
The Department of Statistics strongly discourages students from enrolling in any courses whose regular class meeting dates and times overlap with each other. This policy is in alignment with the College of Letters and Sciences Course Attendance Policy. It is also consistent with the Class Attendance Policy for Students at UW-Madison (https://kb.wisc.edu/ls/24628), whose first sentence reads, "It is expected that every student will be present at all classes." Statistics instructors may opt not to make any alternative arrangements in the event any conflict arises due to a student taking a course with class meetings that overlap with a Statistics course, including a conflict between two Statistics courses. Note that final exams occasionally are scheduled simultaneously for courses which meet at different times; in this situation, please contact your instructor well before the exam date about potential accommodations.

Accommodations for Students with Disabilities
The University of Wisconsin-Madison supports the right of all enrolled students to a full and equal educational opportunity. The Americans with Disabilities Act (ADA), Wisconsin State Statute (36.12), and UW-Madison policy (UW-855) require the university to provide reasonable accommodations to students with disabilities to access and participate in its academic programs and educational services. Faculty and students share responsibility in the accommodation process. Students are expected to inform me of their need for instructional accommodations during the beginning of the semester, or as soon as possible after being approved for accommodations. I will work either directly with you or in coordination with the McBurney Center to provide reasonable instructional and course-related accommodations. Disability information, including instructional accommodations as part of a student's educational record, is confidential and protected under FERPA. (See: https://mcburney.wisc.edu/).

Academic Calendar & Religious Observances
See https://secfac.wisc.edu/academic-calendar/.
Establishment of the academic calendar for the University of Wisconsin-Madison falls within the authority of the faculty as set forth in Faculty Policies and Procedures (https://policy.wisc.edu/library/UW-801#Pol801_ 1_20). Construction of the academic calendar is subject to various rules and laws prescribed by the Board of Regents, the Faculty Senate, State of Wisconsin and the federal government. For additional dates and deadlines for students, see the Office of the Registrar's pages (https://registrar.wisc.edu/dates/). Students are responsible for notifying instructors within the first two weeks of classes about any need for flexibility due to religious observances (https://policy.wisc.edu/library/UW-880).

COVID-19
Information on COVID-19 is constantly changing. Students should be attentive to University communications regarding COVID-19 that may alter instruction and supersede parts of this syllabus.