Course Policies
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 210. You may also find additional information regarding personal emergencies here.
The course website will have an up-to-date course schedule, policies, and slides. Announcements will be sent to the class via Ed (link in the site header).
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) on your case studies. 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 team assignments, you may not directly share code or write up with another team. Unauthorized sharing of the 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.
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.
Duke University is committed to providing equal access to students with documented disabilities. Students with disabilities may contact the Student Disability Access Office (SDAO) to ensure your access to this course and to the program. There you can engage in a confidential conversation about the process for requesting reasonable accommodations both in the classroom and in clinical settings. Students are encouraged to register with the SDAO as soon as they begin the program. Note that accommodations are not provided retroactively.
Grade Assessment
Activities and assessments focus on understanding methods and interpreting results, as well as hands-on analyses of real-world data using R.
Homework (30%)
There are seven homeworks; the lowest grade will be automatically dropped, so only six homeworks count toward your semester grade. The homeworks focus on interpreting results, completing data analyses, and reinforcing concepts and methods from lecture and lab. Feel free to discuss homework assignments with other students -- however, all work must be your own effort and submitted individually. Homework assignments will be completed using Quarto, correspond to an appropriate GitHub repository, and submitted for grading in Gradescope.
All homework assignments automatically have a 24-hour penalty-free grace period for submission. Due to the amount of time you are given to complete the homework and the automatic dropping of the lowest homework grade, I will not accept assignments past the 24-hour grace period for any reason; do not treat the grace period as a modified deadline!
Group Labs (10%)
There are seven graded group lab assignments (though lab itself meets approximately weekly). The lowest grade will be automatically dropped, so only six labs count toward your semester grade. Labs focus on developing programming tools to tackle analysis of real-world datasets, and provide a hands-on way to engage with statistics. Labs will be completed in teams, and all team members are expected to contribute equally to the completion of the assignment and use the team's Git repository as the central platform for collaboration. Commits to this repository will be used as a metric of each team member's relative contribution for each lab, and there will be periodic evaluation on the team collaboration. Like homeworks, lab assignments will be completed using Quarto, correspond to an appropriate GitHub repository, and submitted for grading in Gradescope.
All lab assignments automatically have a 24-hour penalty-free grace period for submission. Due to the amount of time you are given to complete the homework and the automatic dropping of the lowest homework grade, I will not accept assignments past the 24-hour grace period for any reason; do not treat the grace period as a modified deadline!
Exams (45%)
Three take-home, open-note exams test understanding and implementation of methods learned in class, and are worth 15% of your semester grade each. Exams will be turned in electronically on Gradescope. On these exams, you will be asked to use your programming skills from homeworks and labs to carry out a data analysis - I will provide the dataset prior to the exam so you can be familiar with any dataset used on the exam prior to seeing it.
These exams are individual assignments. Absolutely no communication regarding the exam is allowed with anyone except the instructor. More details will be provided as exam dates approach.
Exam dates cannot be changed and no make-up exams will be given. There is no grace period for exam submissions.
Final Group Project (15%)
The final project is an open-ended statistical analysis that answers a research question of interest using a real-world dataset and will be completed with your lab teams. More details on the assignment will be provided during the semester. Note that this final project is assigned in lieu of a final exam; thus, you must submit the project on time by the final exam period in order to pass the course. There is no grace period for final project submissions.
A note on group work
You will be randomly assigned to a team at the beginning of the semester. You are encouraged to sit with your teammates in lecture and you will also work with them in the lab sessions. All team members are expected to contribute equally to the completion of the labs and project and you will be asked to evaluate your team members throughout the semester. Failure to adequately contribute to an assignment will result in a penalty to your grade relative to the team’s overall mark.
You are expected to make use of the provided GitHub repository as their central collaborative platform. Commits to this repository will be used as a metric (one of several) of each team member’s relative contribution for each project.
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:
| Homework (individual) | 30% |
| Lab Assignments (group) | 10% |
| Exams (individual) | 45% |
| Project (group) | 15% |
A letter grade will be assigned as follows:
| 93 | ≤ | A | ||
| 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 |
| 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.
Regrade requests must be made within two days of when a report is returned. These will be honored only if points were tallied incorrectly, or if you feel part of your report is correct, but it was marked wrong (these things do happen!). No regrade will be made to alter the number of points deducted for an identified statistical issue. When a regrade request is evaluated, if new errors are identified, additional points may be deducted from the grade.
Textbooks
There is no official textbook for the course. However, I encourage you to use the following freely available online textbooks as additional references:
- Introduction to Modern Statistics, by Mine Çetinkaya-Rundel and Johanna Hardin
- R for Data Science, by Garret Grolemund and Hadley Wickham
- Tidy modeling with R, by Max Kuhn and Julia Silge
- Beyond Multiple Linear Regression, by Paul Roback and Julie Legler
Late Work Policy
If homework or labs are turned in within 24 hours of the due date/time, then there is no penalty (essentially you have a 24-hour grace period). However, due to the fast-paced nature of this course, absolutely no late work will be accepted beyond this grace period. Again, this grace period applies to homework and labs, but does not apply to the midterm exams or the final project.
Manage your time wisely. Do not treat the grace period as a "modified deadline."
Support
Please be comfortable seeking help! If you have a question during lecture or lab, feel free to ask it! There are likely other students with the same question, so by asking you will create a learning opportunity for everyone. The teaching team is here to help you be successful in the course, and you are also encouraged to attend office hours during the times posted on the home page to ask questions about the course content and assignments. A lot of questions are most effectively answered in-person, so office hours are a valuable resource. I encourage you to take advantage of them!
Outside of class and office hours, any general questions about course content or assignments should be posted on Ed Discussion, which is linked here. If you have questions about personal matters that are not appropriate for the class discussion forum (e.g. illness, accommodations, etc.), you may email the instructor. If you do so, please include “[STA 210]” in the subject line. Barring extenuating circumstances, I will respond to STA 210 emails within 48 hours Monday - Friday. Response time may be slower for emails sent Friday evening - Sunday.
Mental Health and Wellness Resources
Student mental health and wellness are of primary importance at Duke, and the university offers resources to support students in managing daily stress and self-care. Duke offers several resources for students to seek assistance on coursework and to nurture daily habits that support overall well-being. If your mental health concerns and/or stressful events negatively affect your daily emotional state, academic performance, or ability to participate in your daily activities, many resources are available to help you through difficult times. Duke encourages all students to access these resources:
- The Academic Resource Center offers services to support students academically during their undergraduate careers at Duke. The ARC can provide support with time management, academic skills and strategies, course-specific tutoring, and more. ARC services are available free to any Duke undergraduate student, studying any discipline.
- DukeReach provides comprehensive outreach services to identify and support students in managing all aspects of well-being. If you have concerns about a student's behavior or health, visit the website for resources and assistance.
- Counseling and Psychological Services (CAPS) provides individual and group counseling services, psychiatric services, and workshops. To initiate services, walk-in/call-in 9-4 M,W,Th,F and 9-6 Tuesdays. CAPS also provides referral to off-campus resources for specialized care.
- TimelyCare (formerly known as Blue Devils Care) is an online platform that is a convenient, confidential, and free way for Duke students to receive 24/7 mental health support through TalkNow and scheduled counseling.
Technology and Course Material Cost Assistance
Students who have limited access to computers may request loaner laptops through the DukeLIFE Technology Assistance Program. Note that we will be using Duke’s computational resources in this course. These resources are freely available to you. As long as your computer can connect to the internet and open a browser window, you can perform the necessary computing for this course. All software we use is open-source and/or freely available. As well, There are no costs associated with this course. All readings will come from freely available, open resources (open-source textbooks, journal articles, etc.).