Course Policies
We have created the course policies and schedule to help reduce stress and promote effective course mastery. If you find that anything is affecting your wellbeing or academic success and you find it difficult to complete your work, please let me and your Academic Deans know as soon as possible. We are happy to work with you to make sure you succeed in STA 440. You may also find additional information regarding personal emergencies here.
The course website will have an up-to-date course schedule, policies, and slides. Detailed assignment introductions will be made available on Gradescope. Announcements will be sent to the class by e-mail, so please check your e-mail regularly.
Academic Integrity
Academic integrity honesty is of paramount importance in this class, and all work must be done in accordance with the Duke Community Standard, reproduced as follows:
To uphold the Duke Community Standard:
- I will not lie, cheat, or steal in my academic endeavors;
- I will conduct myself honorably in all my endeavors; and
- I will act if the Standard is compromised.
By enrolling in this course, you have agreed to abide by and uphold the provisions of the Duke Community Standard as well as the policies specific to this course. Cheating or plagiarism on assignments, lying about an illness or absence, and other forms of academic dishonesty are a breach of trust with classmates and faculty, violate this Standard, and will not be tolerated.
Any violations will automatically result in a grade of 0 on the relevant assignment and be reported to the Office of Student Conduct for further action. Violations may result in additional reductions to semester course grades depending on the magnitude of the offense, e.g., an entire letter grade drop or automatic failing (F) grade for the course.
Occasionally, datasets we are privileged to use in class are confidential and cannot be distributed more broadly or without express permission from the data-granting sponsor. Any unauthorized dissemination or further use of these datasets beyond this class is a violation of the Duke Community Standard.
Reusing code: You are always welcome to use online resources (e.g. StackOverflow, R vigettes, other textbooks, etc.) on your assignments. If you use code from an outside source, either directly or as inspiration, you must explicitly cite where you obtained the code. Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism and a violation of the Duke Community Standard.
On individual assignments, you may not directly share code or write-up with other students. On group assignments, you may not directly share code or write-up with other groups. Unauthorized sharing of code or write-up will be considered a violation for all students involved.
Use of AI Tools
Recent advances in AI tools (using statistics!) have created powerful tools for generating ideas, summarizing ideas, and other tasks. However, at the end of the day, you must thoughtfully engage with the material; use of AI is not a substitute for learning. You may use AI tools in this course, but you must verify and understand your work; do not just cut and paste without understanding. Furthermore, if you use any AI tools, you must cite the specific tool used, provide all prompts you used, and in an appendix, provide the direct output and entire transcript of the generative AI that you used. Failure to do these all of these steps when using AI tools or plagiarizing from AI output will be considered a violation of the academic integrity policy.
Activities & Assessments
Homework assignments (55%)
There will be three homework assignments during the first half (pre-Spring Break) portion of the course. These individual homework assignments will help you develop as a statistician and scientist and will focus on skills that will improve your case study. Further details will be provided as these homeworks are assigned.
Lab abstracts (5%)
Constant practice (both in terms of applications and in terms of mathematical derivations) is the best way to learn course material, and so many classes will contain interactive components and Socratic learning. Thus, there will be three brief "lab abstract" assignments during the first half of the course, which will serve as mini-case studies to help you prepare for the main case study after Spring Break.
These lab abstracts will be graded on a completion/effort basis and will involve different randomized teams for each of the three assignments. As part of these assignments, individual contributions to each submission will be assessed as self-reported by group members. Team members must provide assessments in order to receive credit for an assignment as part of the group’s peer evaluation process.
Group case study (30%)
The group case study will be assigned during the second half (post-Spring Break) of the course. It will involve two submissions by the group: an initial submission, consisting of a written report and reproducible code on the GitHub repository. This first submission will receive an initial grade. After receipt of comments from the instructor and classmates, groups will have the opportunity to write a response to review and submit a revised written report with the ability to earn up to half of the missing points on that component.
Individual contributions to each submission will be assessed, both by group members and by instructor assessment of the GitHub repository commit history. Team members must provide assessments in order to receive credit for an assignment as part of the group’s peer evaluation process.
Note: Individual grades will be modified if peer evaluations suggest that team members make less than half of the contribution expected and if commit history reflects considerable discrepancies between contributions to the team.
Individual project (10%)
Each student will complete an individual project as part of the course. The individual project should use data that have not previously been used by the student in a project, and the analysis should be entirely the student’s own work. Reusing old datasets/analysis is considered a violation of the Duke Community Standard. Any external resources used should be clearly documented. The student may use self-identified data or a resource provided by the instructor.
Grade Calculation
The grading basis for this class is a traditional letter grade according to the standard university policy. The following table presents the contribution of each component to a student's final grade:
| HW 1 (Interpreting results) | 20% |
| HW 2 (Reproducing results) | 15% |
| HW 3 (Peer review) | 20% |
| Lab Abstracts | 5% |
| Case Study | 30% |
| Individual Project | 10% |
A letter grade will be assigned as follows:
| 93 | ≤ | A | ≤ | 100 |
| 90 | ≤ | A- | < | 93 |
| 87 | ≤ | B+ | < | 90 |
| 83 | ≤ | B | < | 87 |
| 80 | ≤ | B- | < | 83 |
| 77 | ≤ | C+ | < | 80 |
| 73 | ≤ | C | < | 77 |
| 70 | ≤ | C- | < | 73 |
| 67 | ≤ | D+ | < | 70 |
| 63 | ≤ | D | < | 67 |
| 60 | ≤ | D- | < | 63 |
| 0 | ≤ | F | < | 60 |
These posted cut points are guaranteed minimums. This course is not graded to a pre-specified distribution (i.e., "curved"); if every student earns a 95 in the course, then every student will receive an A.
Late Work Policy
There is no penalty if assignments (excluding the final project) are turned in within 24 hours of the due date/time (essentially you have a 24-hour grace period for these assignments). However, due to the fast-paced nature of this course, absolutely no late work will be accepted beyond this grace period.
Manage your time wisely. Do not treat the grace period as a "modified deadline."
Diversity, Inclusion, and Accessibility
It is my intent that students from all diverse backgrounds and perspectives be well-served by this course, that students' learning needs be addressed both in and out of class, and that the diversity that the students bring to this class be viewed as a resource, strength and benefit. It is my intent to present materials and activities that are respectful of diversity and in alignment with Duke’s Commitment to Diversity and Inclusion. Your suggestions are encouraged and appreciated; please let me know ways to improve the effectiveness of the course for you personally, or for other students or student groups.
Furthermore, I would like to create a learning environment for my students that supports a diversity of thoughts, perspectives and experiences, and honors your identities. To help accomplish this, if you feel like your performance in the class is being impacted by your experiences outside of class, please don't hesitate to come and talk with me. If you prefer to speak with someone outside of the course, your Academic Dean is an excellent resource. I, like many people, am still in the process of learning about diverse perspectives and identities. If something was said in class (by anyone!) that made you feel uncomfortable, please talk to me about it.
If you are a student with a disability and need accommodations for this class, it isyour responsibility to register with the Student Disability Access Office (SDAO) and provide them with documentation of your disability. SDAO will work with you todetermine what accommodations are appropriate for your situation. Please notethat accommodations are not retroactive and disability accommodations cannot beprovided until a Faculty Accommodation Letter has been given to me. Pleasecontact SDAO for more information: sdao@duke.edu or access.duke.edu.
University policy permits students to be absent from class to observe a religious holiday. Accordingly, the University has established procedures for students to notify their instructors of an absence necessitated by the observance of a religious holiday. Please submit requests for religious accommodations at the beginning of the semester so we can work to make suitable arrangements well ahead of time.