Instructor:
Class Meetings:
Office Hours:
Mentor Information:
Gradescope Course numbers
Course Description:
This course covers the application of basic statistical methods such as univariate graphics and summary statistics, basic statistical inference for one and two samples, linear regression, one- and two-way ANOVA, and categorical data analysis. Students will use statistical software to analyze data and conduct simulations. (You will learn all these terms throughout the semester!)
Texts:
All additional materials will be posted on the course website.
There may be recommended readings from other sources which will be provided as necessary.
This course aims to introduce students to the field of statistics, including its vocabulary and fundamental principles. The course will prepare students to read, recognize, interpret, and discuss statistical concepts and their use in scientific applications. The course will provide students an understanding of the role of statistics within the scientific method, and provide students the tools to use data to make informed conclusions.
After completing this course, students should be able to:
Engagement and Participation - 5%
Participation in a lab-heavy course is critical. During labs you are expected to help your partner(s) learn the material (which goes beyond simply answering the lab questions), and they are expected to help you. There is no formal attendance, but we will periodically have short, completion-based quizzes at the beginning of class. Failing to submit more than 70% of these will result in a 5% deduction in your final course grade.
Labs - 20%
In-class labs contain embedded questions that you and your lab partner(s) will answer together. Some lab questions will be scored for accuracy with feedback given, while others may be scored for effort/completion. Additionally, labs are to be completed collaboratively, and if it becomes clear that you or your partner(s) are using a “divide and conquer” approach to answering lab questions your score on that assignment will be penalized.
All labs are worth 5 points, and will be due at 10:00pm two days after we are no longer working on it in class, but my goal is for everyone to have them finished before class is over. Your two lowest lab scores will be dropped.
Individual Homework - 20%
Homework will be assigned at most once per week. Absolutely no late homework will be accepted, but your lowest two scores will be dropped. Homework will be due on Gradescope, submitted as a pdf, generally due 10pm on Fridays. Gradescope requires that you indicate which problems appear on which page (we will go over an example) – failure to do this will result in a 25% deduction of that homework’s final score.
I encourage you to work with other students or visit the Math Lab for help on homework questions, but you should clearly understand all your answers and your assignment should be entirely in your own words. The homework is intended primarily as an exercise to practice and develop your understanding and to assist in identifying weaknesses: you do yourself a disservice if you submit work that you do not fully understand.
If you engage in significant collaboration with classmates or tutors, you must explicitly acknowledge that person(s) on the top of your assignment (again, you are encouraged to collaborate, and I want to emphasize that there is no penalty for doing so).
Exams (3)* - 20%, 15%, 10%
There will be 2 midterms and a final exam. Each midterm will focus on a specific set of content, but because course topics are naturally cumulative there may be some questions involving earlier content. The 3 exams will contribute 20%, 15%, and 10% towards your end-of-semester grade, with your highest exam score (of the 3) contributing 20%, the second highest exam score contributing 15%, and the third highest exam score contributing 10%.
Exams will be announced at least two weeks in advance. Exams will be closed notes, but you will be permitted to bring a note card (4in x 6in) and a calculator (no cellphone use allowed). You will not be responsible for writing your own R code on exams, but you should expect to encounter output from R as well as the code that produced it during each exam.
Alternative exam arrangements need to be made at least one week in advance of the time you plan to take the exam; this includes taking the exam in a different location, or times going beyond the given class time. Alternative arrangements are not guaranteed unless proper notification is given.
Final Project - 10%
There will be an ongoing group project throughout the semester. The project will include a few short progress reports before culminating in a short in-class presentation and a three (3) page written report in the last week of the semester. More details will be announced later.
Class Sessions
The core component of our class meetings will be working through hands-on labs in a paired programming environment. These pairs will be assigned during the first half of the semester. After the first project, you will have the freedom to choose your partner, or work independently near someone that you can occasionally consult with. During labs it is essential that you and your partner(s) work together, making certain that each of you understand your work equally well.
Most labs will begin with a brief “preamble” section that we will go through together as class. The purpose of this section is to introduce the topic of the lab and ensure a smooth start to each class meeting.
Attendance
Because this course involves some amount of group work, absences impact not only yourself but also your classmates. That said, I understand that missing class is sometimes necessary. I will not take attendance directly, but labs and the short quizzes occasionally being missed can affect participation and lab scores. There is leeway built into these (see Participation and Labs above). If you must miss class, it would be polite and civil to let your lab partner know about it ahead of time if possible.
Software
Software is increasingly an essential component of statistics and
will play a role in this course. We will primarily use R,
an open-source statistical software program.
You are welcome to use your own personal laptop, or a Grinnell
College laptop, during the course. R is freely available
and you can download it and it’s UI companion, R Studio,
here (note: R must be downloaded and installed before
R Studio):
R from http://www.r-project.org/R Studio from http://www.rstudio.com/You may also work on a classroom computer, all of which will have
R and R Studio pre-installed.
Academic Honesty
At Grinnell College you are part of a conversation among scholars, professors, and students, one that helps sustain both the intellectual community here and the larger world of thinkers, researchers, and writers. The tests you take, the research you do, the writing you submit-all these are ways you participate in this conversation.
The College presumes that your work for any course is your own contribution to that scholarly conversation, and it expects you to take responsibility for that contribution. That is, you should strive to present ideas and data fairly and accurately, indicate what is your own work, and acknowledge what you have derived from others. This care permits other members of the community to trace the evolution of ideas and check claims for accuracy.
Failure to live up to this expectation constitutes academic dishonesty. Academic dishonesty is misrepresenting someone else’s intellectual effort as your own. Within the context of a course, it also can include misrepresenting your own work as produced for that class when in fact it was produced for some other purpose. A complete list of dishonest behaviors, as defined by Grinnell College, can be found here.
Inclusive Classroom
Grinnell College makes reasonable accommodations for students with documented disabilities. To receive accommodations, students must provide documentation to the Coordinator for Disability Resources, information can be found here. If you plan on using accommodations in this course, you should speak with me as early as possible in the semester so that we can discuss ways to ensure your full participation in the course.
Religious Holidays
Grinnell College encourages students who plan to observe holy days that coincide with class meetings or assignment due dates to consult with your instructor in the first three weeks of classes so that you may reach a mutual understanding of how you can meet the terms of your religious observance, and the requirements of the course.
Pregnancy Related Conditions, Title IX
Grinnell College is committed to compliance with Title IX and to supporting the academic success of pregnant and parenting students and students with pregnancy related conditions. If you are a pregnant student, have pregnancy related conditions, or are a parenting student (child under 1-year needs documented medical care) who wishes to request reasonable related supportive measures from the College under Title IX, please email the Title IX Coordinator at titleix@grinnel.edu. The Title IX Coordinator will work with Disability Resources and your professors to provide reasonable supportive measures in support of your education while pregnant or as a parent under Title IX.
Getting Help
In addition to visiting office hours and completing the recommended readings, there are many other ways in which you can find help on assignments and projects.
The Data Science and Social Inquiry Lab (DASIL) is staffed by mentors who are experienced in R programming and may be able to troubleshoot coding problems you are having.
The Grinnell Math Lab is located on the 2nd floor of Noyce Science Center in Room 2012 and offers drop-in statistics tutoring.
The online platform Stack Overflow is a useful resource to find user-generated coding solutions to common R problems. Nearly all professionals have needed to “look up” a coding strategy on a site like Stack Overflow at some point in their career, and I have no problem with you doing the same on assignments or projects. However, if you make substantial use of a Stack Overflow answer (ie: actually integrating lines of code written by someone else into your work, not just getting help identifying the right functions/arguments) the expectation is that you cite or acknowledge doing so.
Large Language Models
Large language models, such as ChatGPT, Bing Chat, or Bard, can be a useful tool for explaining and fixing errors in your R code, or helping you understand example code. You are welcome to use these tools; however, you are ultimately responsible for the accuracy of any code or written work you submit. Relying upon a large language model to write for you is a risky endeavor. The model may provide inaccurate information or generate text that is superficial and lacking sufficient detail. I encourage you to read Professor Erik Simpson’s write-up on writing with LLMs to see some reasons why you shouldn’t lean too heavily on these technologies. Nevertheless, you’re welcome to use large language models in this course in the same way you’d use a website like Stack Overflow or a peer mentor. Overly relying on these tools may set you up to do poorly on exams.
Note: My stance on LLM usage through the course may change, but you will be given notice and it will not be used punitively.