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, that guide 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 lecture videos, linked from the quiz, to guide you through the notes and prepare you for quiz questions 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 the Q&A time that best fits your schedule. 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 office hours)
Instructor:
Gillett, John (Teaching Faculty)	Tu 10:55-11:45, Th 12:15-12:45 and 2:25-3:15 in Morgridge 4508	jgillett@wisc.edu
Teaching Assistants:
Li, Sixu	Mo 3:30-4:20, Fr 4:00-4:50 in Morgridge B2586	sli739@wisc.edu

Class Times
This course meets during session AEE (9/3/25-10/5/25) from 9/4/24 to 10/2/24.

LEC 303-001, -002: TuTh 9:30-10:45 in Morgridge 2522

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 Hadley Wickham, Mine Cetinkaya-Rundel, and Garrett Grolemund

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 computer labs that have RStudio on their Windows machines.

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.

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
Online exam 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.
  • 100 hours (4 days and 4 hours) late gets a 100% penalty.
• The exam deadline is real, with no late submission permitted.

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 via piazza or in Q&A web conferences, submit homework solutions on time, and review well for the exam.

Academic Policies and Statements

• Academic Calendar and Religious Observances
• Academic Integrity

  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/your-career/ethical-guidelines-for-statistical-practice), 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.
  By registering for this course, you are implicitly agreeing to conduct yourself with the utmost integrity throughout the semester.
• Accommodations for Students with Disabilities
• Course Evaluations
• Diversity and Inclusion
• Mental Health and Well-Being
• Privacy of Student Records and the Use of Audio Recorded Lectures
• Remote Proctoring with Honorlock (if applicable)
• Students' Rules, Rights and Responsibilities
• Teaching & Learning Data Transparency
• 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 for Undergraduate Studies, Professor Jessi Cisewski-Kehe (jjkehe@wisc.edu) / Director of Graduate 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/. 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 & Diversity Committee, Professor Karl Rohe (karl.rohe@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/.

• Grade Appeals

  The Department of Statistics has a process whereby students may appeal their final letter grades in Statistics courses on grounds that the grade was calculated incorrectly or inconsistently based on the grading standards stated in this syllabus. This process is in alignment with the College of Letters and Sciences policy upholding the right of students to appeal their final course grades.

  If you believe you received an incorrect final letter grade in this course because the syllabus grading policies were applied incorrectly or inconsistently, then the first step of the process is to appeal your final grade to me. Your appeal must be:

  1. In writing
  2. Submitted within 3 weeks of the Office of the Registrar posting the grades
  3. Must explain in detail why you think the syllabus grading standards were applied incorrectly or inconsistently
  4. Include an explanation of the grade you believe you should have received had these standards been applied correctly and/or consistently

  I will respond in writing to all timely appeals made on valid grounds.

  Should I deny your appeal, you have the option to take your appeal to the Statistics Department's Appeals Committee.

  Note: this appeals process applies to final letter grades only. It is not intended for requesting regrades on individual assignments or exams! To do so, you may contact me or the TA directly. For other kinds of concerns, please check out the Complaints section of this syllabus, or contact me directly.

• Overlapping course time statement

  The Department of Statistics strongly discourages students from enrolling in any courses whose regular class meeting dates & 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.

• Statement on the use of ChatGPT and other generative artificial intelligence (AI) models

  While the Statistics Department recognizes the potential benefits of AI, its use in academic work can be problematic. In this course, two rules regarding the use of ChatGPT and other generative AI 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 use of AI-generated responses as sources of information, even with documentation and attribution, is prohibited.