Philosophy

This course is centered on the idea that you will better understand and retain important statistical concepts if you build your own knowledge and practice using it, rather than by memorizing and regurgitating a set of facts. In order to actively construct knowledge in statistics, you must:

• Engage in the material and think carefully about it; there are rarely rote, black and white solutions in statistics.

• Become skillful at using R, a software package for exploring, modeling, and making decisions with data. You’ll have opportunities to use R inside and outside of class.

• Expect small amounts of daily homework, decent-sized weekly homework sets, and longer projects which allow you to pull your knowledge together.

Class Preparation and Participation

My expectations of you before each class:

1. Read the appropriate sections in the textbook (reading guides are available on Moodle, but I will not collect/grade them).

2. Complete any online tutorials assigned for that day.

3. Read any supplemental material posted.

I expect your participation in our classes. They will often include small and large group work. Occasionally you will be asked to lead a small group on a worksheet or task. This is not meant to intimidate you, put you on the spot, or force you to be the only contributor that day. It is meant to develop your leadership skills, promote interaction with classmates, and give me a chance to hear everyone’s voice. This will be a safe environment where mistakes and uncertainty are welcome!

Zoom behavior

Zoom is terrible… I’d much rather see you in person and watch your eyes roll at my terrible jokes. But, we’re doing the right thing this semester by not meeting in person. The hardest thing for me as a teacher is the mute button. Class is so much better when I can see and hear you. So I’m not requiring you to have your video on during class, but I highly encourage it (feel free to use a background if you’d like). And please unmute yourself when you have something to say.

Computing

This course will use R extensively. Course datasets and code are easily available on the R server which can be accessed by the link on Moodle. Supplemental materials will be provided on Moodle for learning to use R. Further instructions will be provided during class.

Learning R is necessary to do statistics. There is a learning curve and you will make mistakes, it just happens (and still happens to me). It is important that you take a breath, step back, and remember to seek help to resolve your issue. Come to my office hours, ask classmates for help, and ask me questions in class. Past students have found learning R to be ultimately very useful and even fun. R is freely downloadable for both Mac and PC at https://cran.r-project.org/, and it’s available on a St. Olaf server!

Often during our classes, I will ask you to share your screen and code. That means you may have to split your screen to watch and type at the same time. Keep up with the class or your group and be prepared to talk through your code or output.

Homework

There will be weekly homework assignments. Homework assignments are designed to give you practice applying new statistical concepts to new data contexts. Homework will be drawn from the exercises at the end of each chapter as well as additional questions. The homework assignments are long, so you should work through them as we go along. Many of the problems require computation. Code snippets in these cases will usually be provided.

• You are encouraged to discuss problems together, but each person must hand in their own work.

• You must show your work for full credit.

• Homework is ALWAYS due by 11:59 PM each Monday. Anything after this will be assessed a late penalty. This includes late assignments due to technical issues. Plan ahead.

• I expect that you will start soon after receiving the assignment. The assignments are definitely not designed to be one-night jobs.

• Homework is completed using RMarkdown and you are required to upload both the RMarkdown (.Rmd) file and knitted pdf file to Moodle.

Quizzes

We will have several short Moodle quizzes each week. You will have 1 hour to complete the quiz and two attempts. You must complete the quiz independently. You may use the textbook, notes, slides, worksheets, tutorials, or any other materials when taking the quizzes. All weekly quizzes must be completed by 11:59 PM each Monday.

Exams

The midterm and final exams will be take-home to be completed individually. I will make the exam available on Friday after our class, and it will be due by 11:59 PM the following Sunday. The final will follow a similar timeline, only due during our scheduled final time slot.

These exams will focus on your abilities to use the statistical software, to interpret results, to express an understanding of statistical concepts, and to engage in statistical thinking on open-ended questions. They will not focus on plug-and-chug mathematics or hairy mathematical proofs. Make-up exams will be granted only under very special circumstances, and only if arranged in advance.

Midterm Project

Statistics is best learned by getting hands-on with real data. And while learning the correct analysis is a part of this course, communicating and reading statistical analysis is another large part. You will complete one midterm project for this class. It will be a short (~2 page) summary of exploratory data analysis for a data set. I will provided data options and guidelines shortly after we begin the semester.

All projects will be performed in small groups of 2-3 to be determined by myself with your input.

Final Project

This will be a more complete report and in depth analysis of a research question using data either found or provided. You will be expected to submit a project proposal, perform background research, develop a methods description, report your results, and discuss the implications of your findings. You will go through multiple drafts and perform peer reviews for classmates.

All projects will be performed in small groups of 2-3 to be determined by myself with your input.

Participation

Participation in this class can take several different forms: participation in discussions, contributing to small group activities, peer reviews of projects, attending office hours, and plenty of other ways. Be prepared to actively engage in all of these areas for full participation credit.

Note about Disabilities

I am committed to supporting the learning of all students in my class. If you have already registered with Disability and Access (DAC) and have your letter of accommodations, please meet with me as soon as possible to discuss, plan, and implement your accommodations in the course. If you have or think you have a disability (learning, sensory, physical, chronic health, mental health or attention), please contact Disability and Access staff at 507-786-3288 or by visiting wp.stolaf.edu/academic-support/dac.

Statement of Inclusivity

In keeping with St. Olaf College’s mission statement, this class strives to be an inclusive learning community, respecting those of differing backgrounds and beliefs. As a community, we aim to be respectful to all citizens in this class, regardless of race, ethnicity, religion, gender or sexual orientation.

I acknowledge that my gender, race, and education contributes to my privilege hazard. I am committed to learning of and about inherent biases in my teaching and my field, and while they may not be intentional, they are not acceptable. I hope you will be comfortable approaching me if you feel any aspect of this class inhibits your ability to be equally heard and represented. Statistics can be a powerful tool to place a spotlight on the societal inequities we face, but we also need to confront the ways that data are racist, sexism, and classist. Throughout this course, we will use and discuss data that may make you feel slightly uncomfortable, but the goal is to approach important, complex questions through empirical data analysis.

Note about Academic Integrity

Plagiarism, the unacknowledged appropriation of another person’s words or ideas, is a serious academic offense. It is imperative that you hand in work that is your own, and that cites or gives credit to others whenever you draw from their work. Please see St. Olaf’s statements on academic integrity and plagiarism at: https://wp.stolaf.edu/thebook/academic/integrity/. See also the description of St. Olaf’s honor system at: https://wp.stolaf.edu/honorcouncil/

St. Olaf’s Academic Integrity Policy, including the Honor System, is an integral part of your academic experience. I consider any violation of this code to be extremely serious and will handle each case appropriately. Here are some guidelines for this class. They do not cover all eventualities so if you have any doubts about a course of action you can ask me.

• Homework assignments may be done in collaboration with other students (this is highly encouraged). However, the final product must written by you, in your own words, unless group assignments have been specifically allowed.

• In no event can you copy answers from another student, a website, solutions manuals, or elsewhere.

• Exams and quizzes MUST be completed independently. Any suspicion of collaborative work will result in reporting to the Honor Council and potential for immediate failure of the course.

• When you sign your pledge on an exam that you have “neither given nor received assistance, and seen no dishonest work” I treat your signature as your solemn pledge that all your actions have been honorable. For example, if we have a take-home exam, you are assuring me that you shared no information with others, that you did not solicit or receive help from anyone besides me, etc.

• Don’t treat the honor code lightly; if you’re in doubt about a possible violation, ask me.