Introduction to Mathematical Statistics

Description from DukeHub

Formal introduction to basic theory and methods of probability and statistics: probability and sample spaces, independence, conditional probability, and Bayes' theorem; random variables, distributions, moments, and transformations. Parametric families of distributions and central limit theorem. Sampling distributions, traditional methods of estimation, and hypothesis testing. Elements of likelihood and Bayesian inference. Basic discrete and continuous statistical models. Not open to students who have credit for or are taking Statistical Science 230, 231, 240, 250, or 432. Not open to undergraduate students. Does not count towards the MS in Statistical Science degree.

Skills

• data and statisticsdescription: "probability" and 8 more

AI-resilient: 0 of 5 0/5

• No: Physical, lab or field work
• No: Live work with people
• No: Judgment someone is accountable for
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

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