A.4 Practical matters

A.4.1 Expectations

Class ground rules: This course operates in a safe, respectful, and inclusive environment. That applies to class meetings, office hours, discussion postings, and any other communications you send as part of this course. As a college and a community, we value diversity of age, race, ethnicity, gender identity and expression, sexual orientation, religious affiliation, disability, and other identities, visible or nonvisible.

If at any time during this course you are made to feel uncomfortable or unwelcome, I encourage you to talk to me about it. You can also use one of the other contact methods described in the resources.

Academic integrity and the honor code: You’re responsible for doing your work in this course both in accordance with stated rules (which may be different for different assignments) and with a sense of fairness and integrity. The section of the student handbook dealing with the Honor Code can be found here: https://www.mtholyoke.edu/student-handbook/student-accountability.

Very briefly, academic integrity means that you do not seek any unfair advantages over other students, and that all work you present as your own is your own. But the details of that can get complicated. I’ve tried to be very clear about what collaboration and resources are appropriate for each assignment, but if you have any questions or uncertainties about what something means, or whether something is okay, please contact me. I am very happy to talk to you about this. I am much, much happier to talk to you about it before something happens.

One issue that people tend to struggle with in this course is plagiarism. Yes, it is possible to commit plagiarism in stats or math! Sure, there are fixed vocab words and equations that everyone uses, but your explanations and discussions should always be your own creation. Any time you take someone else’s words/ideas without citing them is plagiarism – even if it’s just my notes, or the built-in R documentation. Again, I’m happy to talk with you about this. You can also find a helpful guide to avoiding plagiarism here: https://plagiarism.arts.cornell.edu/tutorial/index.cfm.

Prerequisites: Stat 242 or equivalent – that is, a college-level stats course that spends quality time with hypothesis tests and multiple regression – and a willingness to devote regular time to working on this course. Technically, R experience isn’t a prerequisite, but it’ll make your life a lot easier; let me know right away if you don’t have R experience, so I can make sure you have extra resources to get caught up.

You do not need to have seen experimental design before. You also don’t need to have taken Math 211 (Linear Algebra) or Stat 340 (Regression But This Time For Realsies). We will throw some matrices around during the course, but I will get you up to speed on whatever you need.

In general, if you are ever unsure whether you’re supposed to know something already – or concerned that you don’t – I encourage you to ask me (or a classmate!) about it. Sometimes I actually don’t expect you to know it yet. And if I do expect you to know it, I’m happy to get you the help you need to pick it up.

A.4.2 Access and accommodations

Accessibility: My goal is to create a class that’s accessible, inclusive, and rewarding for everyone. This means accommodating everyone’s disability and accessibility needs, in addition to any logistical issues that may come up.

If anything like this applies to you, come and talk to me. Sooner is better! You can also get in touch with the folks over at Disability Services (https://www.mtholyoke.edu/directory/departments-offices-centers/disability-services) for much more help.

Exceptions, extensions, etc.: Given the world in which we find ourselves, this course is already designed to be flexible (see more here). But if something comes up that stretches that built-in flexibility, tell me about it as soon as possible. We can often work out an alternative plan or accommodation, but there is much less I can do if you come to me after the fact. See here for more on this.

A.4.3 Materials

Books: There are two required textbooks for this course: Box, Hunter, and Hunter’s Statistics for Experimenters, second edition (which I will call “BHH”), and Goos and Jones’ Optimal Design of Experiments: A Case Study Approach (which I will call “GJ”).

Between you and me, you can totally find a scanned version of BHH online for free, if you don’t mind the lower graphical quality. (Make sure you’re using the second edition, which is substantially different from the first.) But I listed it as required so it’s covered by your financial aid if you want a hard copy. As far as I know, you’ll actually have to buy GJ.

Any supplementary resources will be available for free or in the library. We’ll also draw on my own notes for some topics.

We’ll use R and RStudio for coding, including activities, practice problems, and projects. By default, I expect everyone to use RStudio Server (MHC has an institutional account so you have access at no cost); if you won’t be able to use the server, talk to me. (Note that you will need to be on an MHC network or connected to the VPN to access MHC’s server! Ask me or LITS if you have questions about this.)

Although you won’t need R on your own machine, you will need to do a lot of typing and have multiple things open at once. I recommend you have access to an actual computer unless you’re really good with a teeny tiny keyboard, though you can certainly use your phone/tablet for some things during the course. If you have any questions or issues around devices (including accessibility issues, restrictions on screen use, not having a laptop to bring to class, etc.) let me know.