Syllabus and Course Details
MATH 3080 - Spring 2024
This page constitutes the official syllabus and course policy.
Course Information
Title: Introduction to Statistical Inference
Code:: Math 3080 - Spring 2024
Lecture: MWF 2:00pm - 2:50pm - Richardson 108
Recitation: Th 8:00am - 9:15am - Gibson 126
Webpage: https://math.tulane.edu/~spunshonsmith/MATH3080-Spring24/index.html
Course Staff
Instructor: Sam Punshon-Smith
Office: Gibson 423
Email: spunshonsmith@tulane.edu
Office Hours: : M 10am - 11am, W 3pm-4pm
Teaching Assistant: John Argentino
Office:Gibson 415F
Email: jargentino@tulane.edu
Office Hours: Th 11am-1pm
Description
MATH 3080 is mathematically-grounded first course in statistical inference. We’ll dive into topics like sampling distributions, parameter estimation, confidence intervals, hypothesis testing, linear regression, Bayesian methods, and categorical data analysis. It's not only useful for those looking to delve deeper into probability and statistics, but also for students interested in using statistical methods for research in the social, biological, or physical sciences. The course will also contain a computational component using the statistical software “R”. Friday lectures will often include an R session that will work through examples to gain familiarity with the computational side of statistics.
For a detailed outline of the course topics by week, see the course schedule page.
Course Goals
By the end of the course, students will
- demonstrate an understanding of fundamentals of mathematical statistical inference
- gain proficiency in the application of statistical inference to real datasets
- learn to apply mathematical and statistical reasoning to solve problems
- gain basic data literacy to aid in interpreting and presenting real world data
Prerequisites
Students are expect to be fluent in single and multivariate calculus and have taken an introductory course in probability theory. Official prerequisites are:
Please refer to the above links to the course catalog for a list of topics associated with each course.
Course Materials
We will be using the following textbook for assigned readings:
Required Technology
R
An open-source statistical computing environment available for all operating systems. The R software may be downloaded at no charge from http://www.r-project.org/.
Course Webpage
The course webpage will be the central information point in the class. It will contain the most up-to-date versions of the syllabus, weekly course schedule, office hours schedule, homework assignments and solutions, notes and supplementary resources. You will need a modern, up-to-date browser with javascript enabled to take full advantage of the material.
Ed Discussion
We will be using Ed Discussion for most course communication. Here you can post questions about the course material here, and course staff or students can answer. Please see the Communication and Help section below for more details.
Canvas
Canvas will be used to keep track of grades and post/receive assignments. Important time-sensitive announcements may also be made through Canvas in addition to Ed Discussion.
Course Logistics
Lecture
Lectures are a major component of the class. The lectures are in person MWF from 2-2:50pm unless stated otherwise and will cover the material according the course schedule. Regular attendance at lectures is expected. Excessive unexcused absenteeism will result in the submission of an absence report form to the dean’s office.
Recitation
Weekly recitations will take place in-person TH 8-9:15am and will be conducted by the TA. The session will include worked examples not covered in lecture and a review of the homework assignments from the previous week. Midterm and Exam reviews will also be conducted during this time. Recitations are not mandatory, but are highly encouraged.
Time expectations
Students are expected to spend 3 hours a week on lectures, 1:15 hours a week on recitation, 3 hours a week on assigned course readings, and 6 hours a week on homework assignments and optional R labs.
Communication and Help
Ed Discussion
We will be using Ed Discussion for all course communication and discussion. You should have received an email to sign you up at your tulane email address. You can use it to ask questions about course content, assignments or general logistics. You can post publicly (anonymously to other students if you wish) to the entire class, or privately to the course staff. Please refrain from posting publicly about specific assignment problems before their due date. Public posts can be answered by anyone, including students. If you feel like a question might be of interest to students in the class we encourage you to post it publicly (kind of like asking a question in class). Students are also encouraged to answer each other's posts and to “like” solutions that are useful. To save effort on the part of the staff, please check if your question (or a similar one) has been asked already before posting.
For more information on Ed Discussion, you can refer to the Quick Start Guide.
Office hours
Myself and the TA will hold weekly office hours according to the dates listed in the Course Staff section above.
If you require a private audience to discuss personal matters or concerns about your performance in the course, you may request a personal meeting by request. This is for special circumstances only.
Homework
There will be weekly homework problem sets posted on Canvas and the course webpage. Due dates will also appear in the course schedule.
Homework Submission Guidlines
Homework assignments will be due on Mondays by 6pm (CT) in Canvas. Assignments should be submitted in Canvas by submitting a scanned pdf or typed assignment. You can do this by scanning your handwritten assignment with you phone or camera. We recommend using the Google Drive App, which has a robust scan feature. Please make sure that your pages are in the correct order. Assignments with pages incorrectly assigned to problems will receive -5% . The scans must be clear and easy to read. Use dark lettering that isn't too small and make sure to have good lighting if you are photographing the homework. Make sure that everything is in focus. Problems which are too hard to read may be marked wrong by the TA. Do not submit the homework at the last minute in case you run into technical problems.
Late Policy
A late homework extension window will be available for 24 hours after the homework deadline to account for technical problems. Students who submit the assignment during the extension window will automatically receive a 10% deduction without proper explanation. If you experience technical problems, you must communicate this as soon as possible via Ed Discussion. If you don't communicate the problem, the assignment will receive a penalty or not be accepted depending on the extent of the tardiness. Myself or TA can also submit assignments on your behalf if Canvas is misbehaving.
If you are unable to complete a homework assignment before the due date and don't have a valid excuse, it is recommended that you turn is as much as you can or take the 10pt% penalty by turning it in late. Your lowest homework score will be dropped at the end of the semester.
Independent Work
Students are encouraged to work together but must independently write up their own solutions. Students who are found to have copied part of an assignment will receive a zero on the assignment and may be reported for academic misconduct. See the Code of Academic Conduct for more information on Tulane’s academic integrity policy
Midterms and Final
The course has two midterm exams and a final exam administered in class on dates listed on the course schedule.
Students who are unable to attend regularly scheduled exams must provide advance notification so that a make-up exam may be scheduled. Unexcused absences on exam dates may result in a failing grade for the exam. Note that missing an exam in order to gain an advantage is considered a violation of Tulane’s Academic Code and will be treated as such.
Grading
The breakdown and percentages for all graded materials are described in the following table:
| Category | Percentage |
|---|---|
| Homework and participation | 35% |
| Midterms | 30% (15% each) |
| Final Exam | 35% |
| Total | 100% |
Grades will tentatively be assigned standard cutoffs. Grade modifiers (+-) may be added to grades within ~1% of the boundary points on a case by case basis. Eg: 89% = B+
| Percentage | Grade |
|---|---|
| 90%-100% | A |
| 80%-89% | B |
| 70%-79% | C |
| 60%-69% | C |
| < 60% | F |
Code of Academic Conduct
The Code of Academic Conduct applies to all undergraduate students, full-time and part-time, in Tulane University. Tulane University expects and requires behavior compatible with its high standards of scholarship. By accepting admission to the university, a student accepts its regulations (i.e., Code of Academic Conduct and Code of Student Conduct) and acknowledges the right of the university to take disciplinary action, including suspension or expulsion, for conduct judged unsatisfactory or disruptive.
ADA/Accessibility
Tulane University is committed to offering classes that are accessible. If you anticipate or encounter disability-related barriers in a course, please contact the Goldman Center for Student Accessibility to establish reasonable accommodations. If approved by Goldman, make arrangements with me as soon as possible to discuss your accommodations so that they may be implemented in a timely fashion. I will never ask for medical documentation from you to support potential accommodation needs. Goldman Center contact information: Email: goldman@tulane.edu; Phone (504) 862-8433; Website: accessibility.tulane.edu
Diversity
This course is designed to support an inclusive learning environment where diverse perspectives are recognized, respected and seen as a source of strength. It is our intent to provide materials and activities that are respectful of various levels of diversity: mathematical background, gender, sexuality, disability, age, socioeconomic status, ethnicity, race, and culture.
- 2024