University of Wisconsin-Madison
Statistics 303: R for Statistics I
Summer 2023
Schedule

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.
Here is a more detailed course map.

Requisites
STAT 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
Online:

Teachers
NameOffice HoursEmail (please ask most questions in Q&A or via piazza)
Instructor:
Gillett, John (Lecturer)(see Q&A times in the schedule)jgillett@wisc.edu
Teaching Assistants:
Xiong, Haoran(see Q&A times in the schedule)hxiong45@wisc.edu

Class Times
This online course meets during session ACC (5/30/23-6/18/23) from May 30 June 15, 2022.
Live online help (optional) is available from the teacher and TAs via Zoom at the times listed in the schedule. To attend a Zoom web conference, visit https://canvas.wisc.edu/courses/356407/external_tools/14065 and click "Start" for today's meeting.

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 R, a statistical programming language, and RStudio, a free integrated development environment. In case of computer trouble:

Help
The TAs 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.
During summer, this one-credit course runs in three weeks, which is 1/5 of a regular semester. The weekly workload of this course should be like that of a five-credit, one-semester course: 1 credit = (5 credits/semester)*(1/5 semester).

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:

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. (See the UW Academic Integrity policy.)


Privacy of Student Information & Digital Tools: Teaching & Learning Analytics & Proctoring Statement
The privacy and security of faculty, staff and students' personal information is a top priority for UW-Madison. The university carefully reviews and vets all campus-supported digital tools used to support teaching and learning, to help support success through learning analytics, and to enable proctoring capabilities. UW-Madison takes necessary steps to ensure that the providers of such tools prioritize proper handling of sensitive data in alignment with FERPA, industry standards and best practices.
Under the Family Educational Rights and Privacy Act (FERPA which protects the privacy of student education records), student consent is not required for the university to share with school officials those student education records necessary for carrying out those university functions in which they have legitimate educationl interest. 34 CFR 99.31(a)(1)(i)(B). FERPA specifically allows universities to designate vendors such as digital tool providers as school officials, and accordingly to share with them personally identifiable information from student education records if they perform appropriate services for the university and are subject to all applicable requirements governing the use, disclosure and protection of student data.

Privacy of Student Records & the Use of Audio Recorded Lectures
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.

Students' Rules, Rights, & Responsibilities
During the global COVID-10 pandemic, we must prioritize our collective health and safety to keep ourselves, our campus, and our community safe. As a university community, we must work together to prevent the spread of the virus and to promote the collective health and welfare of our campus and surrounding community.

Diversity & Inclusion
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.

Academic Integrity
By virtue of enrollment, each student agrees to uphold the high academic standards of the University of Wisconsin-Madison; 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 action include, but is not limited to, failure on the assignment/course, written reprimand, disciplinary probation, suspension, or expulsion.

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, and include:

By registering for this course, you are implicitly agreeing to conduct yourself with the utmost integrity throughout the semester.

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 (Faculty Document 1071) require that students with disabilities be reasonably accommodated in instruction and campus life. Reasonable accommodations for students with disabilities is a shared faculty and student responsibility. Students are expected to inform faculty of their need for instructional accommodations by the end of the third week of the semester, or as soon as possible after a disability has been incurred or recognized. Faculty will work either directly with the student or in coordination with the McBurney Center to identify and provide reasonable instructional accommodations. Disability information, including instructional accommodations as part of a student's educational record, is confidential and protected under FERPA. (See: McBurney Disability Resource Center)

Academic Calendar & Religious Observances
See: https://secfac.wisc.edu/academic-calendar/#religious-observances

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 or the Director of Graduate Studies, Professor Bret Larget, bret.larget@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 & Diversity Committee, Professor Karl Rohe (karlrohe@stat.wisc.edu). You may also use the University's bias incident reporting system, which you can reach at https://doso.students.wisc.edu/services/bias-reporting-process/.