Description
An understanding of the commonly used statistical language R. Topics will include using R to manipulate data and perform exploratory data analysis.

Learning Outcomes
Students will use R to manipulate data and perform exploratory data analysis using introductory statistics. A student completing Statistics 303 can do these things:

1. Use basic R vocabulary.
2. Manipulate data in R.
3. Produce graphics and reports.
4. Apply statistical methods.
5. Run basic simulations.

Requisites
STAT 240, 301, 302, 312, 324, 371, STAT/MATH 310, ECON 310, GEN BUS 303, 304, 306, 307, PSYCH 210, or C&E SOC/SOC 360, or graduate/professsional standing or member of the Statistics Visiting International program

Designations and Attributes
Breadth: Natural Science
Level: Intermediate
L&S Credit Type: Counts as Liberal Arts and Science credit (L&S)
Repeatable for Credit: No

Instructional Mode
Classroom instruction

• See this schedule.
• There are eight topics in the schedule labeled "1. R as a Calculator" through "8. Simulation". For each topic, there are links to three types of course materials:
  • There are lecture notes, each consisting of 1- to 3-page ".pdf" file, to guide my lectures and serve as reference sheets.
  • There is an online quiz to provide low-stakes practice. You may repeat it many times. Your best score before its deadline counts. Each quiz includes 5 to 18 questions. Each question can be answered by writing a single line of R code.
  • There are screencast videos, linked from the quiz, to guide you through the notes and prepare you for quiz questions and homework.
    The course uses a "flipped classroom" in which students watch videos before class and then work in class on Q&A, quizzes, and homework with support from peers (for quizzes) and teacher and TA (for quizzes and homework).
• There are four homework assignments, each requiring several to about 100 lines of code.
• There is an exam in class.
• Here are ways to get help:
  • Ask questions in class.
  • Attend office hours.
• For each day in the schedule, do the things listed in its "Before Class" column before class. Complete the things in the "Homework Due" column before 11:59 p.m.

Teachers

Name	Office Hours	Email (please ask most questions in class or via piazza)
Instructor:
Gillett, John (Lecturer)	Tu 3:00-3:50, Th 2:25-3:15 in 1221 Medical Sciences Center	jgillett@wisc.edu
Teaching Assistants:
Chen, Zhifeng	Mo 12:00-12:50, Tu 1:30-2:20 in 1475 Medical Sciences Center	zchen925@wisc.edu

Class Times
This course meets during session AEE (9/4/24-10/6/24) from 9/5/24 to 10/3/24.

LEC 303-001, -002: TuTh 9:30-10:45 in 122 Ingraham

Textbook

No textbook is required. We'll provide course notes and online screencast lectures (and we'll read R documentation and write R code).

Optional Online Reading

R for Data Science by Garrett Grolemund and Hadley Wickham

An Introduction to R (pdf) by W. N. Venables, D. M. Smith and the R Development Core Team

Advanced R by Hadley Wickham (advanced)

Intro to R video lectures by Google Developers

R Programming wikibook

Using R for Data Analysis and Graphics by J. H. Maindonald

The R Inferno by Patrick Burns (advanced)

Optional Reference Books

R for Data Science by Garrett Grolemund and Hadley Wickham

Data Manipulation with R by Phil Spector

Advanced R by Hadley Wickham (advanced)

Introductory Statistics with R by Peter Dalgaard (2008)

R in a Nutshell by Joseph Adler (2009)

A Beginner's Guide to R by Alain F. Zuur, Elena N. Ieno, and Erik Meesters (2009)

Software for Data Analysis: Programming with R by John Chambers (2008) (advanced)

Computing
A computer is required that can run RStudio, a free integrated development environment that supports working with R, a statistical programming language. In case of computer trouble:

• Consider posit Cloud / RStudio Cloud, which runs in a browser.
• UW's InfoLabs offer loaner laptops and desktop computer labs that have RStudio on their Windows machines.

Help
The TA(s) and I are eager to help in synchronous (live online) Q&A and office hours. Please use piazza for asynchronous Q&A help.

Credits and Grades
This is a 1-credit course.
45 Hours Per Credit -- One credit is 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, labs, exams, presentations, tutorials, reading, writing, studying, preparation for any of these activities, and any other learning activities. This 3-credit course meets twice for 75 minutes each week and carries the expectation that students will work on the course for about 3 hours out of class for each 75-minute meeting.

These points are available:

8 online quizzes (Quiz 1, ..., Quiz 8)	≈ 93
4 R or R Markdown scripts (hw1.R, hw2.R, hw3.Rmd, hw4.Rmd)	≈ 70
In-class exam on 10/3/24 on reading and writing R code	≈ 75
Total	238

We'll 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 = 70, AB = 50, B = 30, BC = 20, C = 10, D = 5, F = 0 (That is, performing better than 70% of the class => A. Here is a graph of this percentile curve.) Your grade will be the higher of these two grades.

Grades are recorded at https://canvas.wisc.edu.

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. Regarding late work:

• The quizzes are auto-graded by my web-form program. I will copy their scores to Canvas one day after their deadline; that is, you get one free late day for each quiz. (I recommend solving the quizzes on time, as if you do not, you are falling behind.)
• I have set Canvas to allow late submission of HW1, HW2, HW3, and HW4 with a 1% per hour penalty. For example:
  • 1 second to 1 hour late gets a 1% penalty.
  • 12 hours late gets a 12% penalty.
  • 50 hours late gets a 50% penalty.
• The exam deadline is real.

I encourage you to discuss the course, including the online quizzes, with others, but you must write the R scripts and the exam by yourself and prevent others from copying your work.

How to Succeed in This Course
The successful student will study the online lectures and their notes, solve the (repeatable) quizzes on time, ask questions as needed in class or office hours, submit homework solutions on time, and review well for the exam.

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 Karle Rohe (karl.rohe@wisc.edu). You may also use the University's bias incident reporting system, which you can reach at: https://doso.students.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.