Open readings & attribution

STAT 45203 · what this course draws on, and how

How this course uses sources. The primary material is instructor-authored (this site + Blackboard). The open texts below are conceptual companions, cited by section title only — the course reproduces no prose, figures, examples, or exercises from them, and all data on this site is synthetic and instructor-generated. This is deliberately more restrictive than the open licenses require, so the public/private boundary stays clean. Licenses are recorded as best known and remain subject to instructor confirmation before any wider release.

Why there is no single textbook

The syllabus is explicit that no single textbook carries this course. Resampling, classical nonparametrics, robust statistics, and simulation-based inference each have their own literature, and the best free treatments are scattered across several open books and public-domain references. Rather than adopt one text and stretch it, the course uses a map, don’t mine approach: each week’s ideas are developed originally here, and the readings below are offered as places to read the same idea in another voice and check your understanding.

If you read one of these companions and then re-explain the idea in your own words after checking it against the notes, that is exactly the kind of verification the AI policy asks for.

The open companions

For bootstrap, permutation, randomization, and simulation-based inference

  • Introduction to Modern Statistics — Mine Çetinkaya-Rundel & Johanna Hardin. https://openintro-ims.netlify.app/ · License: CC BY-SA 3.0 US (print) / CC BY-NC-SA 4.0 (eBook edition). Primary conceptual companion for simulation-based inference, randomization, and the bootstrap (Weeks 3–6, 13).
  • Statistical Inference via Data Science: A ModernDive — Chester Ismay & Albert Y. Kim. https://moderndive.com/ · Open online (specific CC terms unconfirmed; treated as cite/link only). A hands-on companion for bootstrap confidence intervals and permutation tests with the infer package (Weeks 3–6).
  • OpenIntro Statistics, 4th ed. — David Diez, Mine Çetinkaya-Rundel, Christopher Barr. https://www.openintro.org/book/os/ · License: CC BY-SA 3.0. Foundations-for-inference and regression-review companion (Weeks 1, 5–6, 11–12).

For rank-based and robust methods

  • Learning Statistics with R — Danielle Navarro. https://learningstatisticswithr.com/ · License: CC BY-SA 4.0. A readable companion for rank tests and robust summaries (Weeks 7–10).
  • NIST/SEMATECH e-Handbook of Statistical Methods — U.S. National Institute of Standards and Technology. https://www.itl.nist.gov/div898/handbook/ · U.S. Government work (public domain, with courtesy attribution). Reference for order statistics, exploratory data analysis, and robustness (Weeks 2, 10–11).

An instructor reference (cited, not reproduced)

  • Hesterberg, T. C. (2015). “What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum.” The American Statistician, 69(4). Preprint: https://arxiv.org/abs/1411.5279. A published article on what the bootstrap does and does not deliver. Used as an instructor reference only — named and linked, never reproduced.

Software

See Software setup for the fastest path to a working environment.

Attribution and licensing note

Where the companions above are licensed CC BY-SA or CC BY-NC-SA, that license would permit adaptation with attribution and share-alike; this course chooses not to adapt their content and instead cites them by section title only, keeping the site’s own content fully instructor-original. Any figures that resemble a standard display (an ECDF, a bootstrap histogram) are regenerated originally from synthetic data, not copied. The instructor confirms license and attribution wording before any public release beyond this draft.