This course provides a rigorous introduction to statistical inference. Topics include basic probability theory (probability measures; discrete and continuous random variables; joint, marginal and conditional distributions; mean, variance and covariance), Bayesian and frequentist perspectives on uncertainty quantification, decision theory, point estimation (Bayesian and maximum likeihood estimation) and hypothesis testing.
Multivariate calculus. Students should be comfortable with integration and differentiation of functions of several variables. Linear algebra is not required for this course but will be for its sequel, STAT 24500.
Required: Rice, John A. Mathematical Statistics and Data Analysis, 3rd Edition.
Additional References:
Probability and Statistics, 4th Eds, by DeGroot and Schervish. (This text, in particular its Chapter 7 provides a more detailed coverage of Bayesian estimation)
The Seven Pillars of Statistical Wisdom, by Stephen M. Stigler. (This is a really great short book that blends history and the fundamental concepts of statistics, giving you the historical contexts behind many of the ideas we will cover in this course.)
All exams will be closed-book and closed-notes. No electronics are allowed and hence the use of the internet or genAI tools are prohibited. A formula sheet will be provided. A letter-sized cheat-sheet (one-sided for the midterms and double-sided for the final) may be brought to the exams.
Ed discussion and other communications: Questions about course content, homework, or exams should be posted publicly (either anonymously or not) so that everyone in class can benefit from the discussion. If you feel that the post may reveal too much of your work for a homework problem that has not been submitted, you can use private posts to reach the teaching team. The teaching team may summarize and repost private questions publicly (while maintaining anonymity) when the answer is broadly relevant. (Please note that your identity is visible to the instructor and TAs even for anonymous and private posts.) Only messages not appropriate for a forum (such as those involving personal or other sensitive information) should be sent to the teaching team directly via email. In such cases, please be sure to include “STAT 24400” in the subject line.
Weekly review: There will be a weekly review session held by the TAs to go over any questions you may have about the course materials. Attendance is optional but highly encouraged.
Late Work: Homeworks that are less than 24 hours late will be accepted with a one-level penalty. No homeworks beyond 24 hours late will be accepted. However, the lowest homework score will be dropped including a missing homework.
Academic Integrity: Students are expected to adhere to the university’s academic integrity policies. See details at https://studentmanual.uchicago.edu. No form of academic dishonesty will be tolerated. Some examples include cheating, plagiarism, and lying about illness or other reasons for absence. Copying solutions from others or from online sources including websites and generative AI tools is considered plagiarism. Sharing, posting, or distributing any course materials publicly without prior permission from the instructor is also prohibited. This includes posting on social media platforms, educational forums, or any other public websites. Violations of the community standards will result in failure of this course and will be reported to the Office of College Community Standards.
Collaboration: Discussions on the homework problems are allowed, but you must write down your own solutions independently.
Regrade requests: If you believe there was an error in the grading of your homework, or exam, you may submit a regrade request on Gradescope. (Requests made through Ed or email will not be accepted.) Such requests will be considered if there was an error in the grade calculation or if you feel a correct answer was mistakenly marked as incorrect. Requests to dispute the number of points deducted for an incorrect response will not be considered. The request must be made in writing within one week of the return of the graded item. Clearly explain the reason for the regrade request. Note that the entire item will be regraded, which may result in a higher or lower score.
Missed midterms: If you have an unchangeable conflict with the midterms, you must arrange with the instructor at least one week prior to the scheduled exam. The make-up midterm in such cases will occur before the scheduled one. If you miss a midterm due to unexpected circumstances such as short-term illness or family emergencies, you must inform me prior to the exam, and provide proper documentation for such occurrences. In such cases there will be no make-up for the missed exam, and your final exam will count for both the missed midterm and the final.
Use of generative AI tools: Use of generative AI tools (e.g., ChatGPT, Gemini, DeepSeek, etc.) for directly solving any graded items is prohibited. General consultation of these tools on course topics to help study and understand the materials is allowed. It should be treated as the same as consulting another human (e.g., the instructor, a TA or classmate) for general understanding and studying the course materials, not for attaining direct answers on graded problems. Violations of this policy will be treated as academic misconduct.