Course Policies
The course website will have an up-to-date course schedule, policies, and slides. Announcements will be sent to the class by e-mail, so please check your e-mail regularly. Visit the course home page for details regarding course logistics and instructional team.
Academic Integrity
All students must adhere to the Duke Community Standard. Duke University is a community dedicated to scholarship, leadership, and service and to the principles of honesty, fairness, and accountability. Citizens of this community commit to reflect upon these principles in all academic and non-academic endeavors, and to protect and promote a culture of integrity.
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 a failing (F) course grade depending on the magnitude of the offense.
Prerequisites
This course is designed for students who have completed a first course in linear algebra (e.g., MATH 221 or equivalent). Students should also have completed an applied regression modeling course (e.g., STA 210 or equivalent) or be taking it concurrently. This course assumes that students are familiar with basic concepts from calculus, primarily differentiation and integration in several variables. No previous probability or mathematical statistics coursework is required or assumed; concepts needed in these areas will be presented in class.
Activities & Assessments
Homework
There are twelve short homeworks assigned approximately weekly, each worth 7% of the semester grade; the lowest two grades will be automatically dropped, so that only ten homeworks count toward your semester grade. Feel free to discuss homework assignments with other students -- however, all work must be your own effort and submitted individually. Homework must be scanned (or typeset) and uploaded to Gradescope as a .pdf file.
Exam
A culminating take-home exam will be given during the final exam period, worth 30% of the semester grade. Absolutely no communication regarding the exam is allowed with anyone except the instructor. The exam date cannot be changed and no make-up exams will be given.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 | 70% |
| Exam | 30% |
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
If homework is 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 homework will be accepted beyond this grace period. Keep in mind that you do get to drop the two lowest assignments. There is no late work accepted for the exam.
Manage your time wisely. Do not treat the homework 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.
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.
Header: The image in the header of this site is from a first edition printing of Carl Friedrich Gauss' work Theoria combinationis observationum erroribus minimis obnoxiae. In particular, it displays one of the concluding sections of the first half of the work, in which Gauss establishes the conditions under which the ordinary least squares estimator achieves the minimum variance among all possible linear unbiased estimators. This is the Gauss-Markov theorem, which, coincidentally, we will derive and study as the conclusion to the first half of our semester.