STA 2212H: Statistical Theory for Data Science II
January 6 to March 31 2026
Tuesday 5.10 pm - 8.00 pm
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Office Hours
Monday 7.00--8.00 pm (Zoom), andTuesday 4.00--5.00 pm (near classroom))
Teaching Assistant
Kathleen MiaoWeek 12 March 31
Week 10 March 17
- Slides
- Slides (with scribbles)
- New Scientist article about CERN discovery (7 sigma!)
- Leek \& Peng on p-values (Nature, 2015)
Week 9 March 10
- Slides
- Slides (with scribbles)
- NY Times article on ermergent misalignment
- Nature paper that was the source
Week 8 March 3
- Slides
- Slides (with scribbles -- some parts still ntbc)
- Online Causal Inference Seminar (Mats Stensrud March 3)
- Wang, Richardson and Robins (2026), J Data Science 24, 53--85
- Josey et al. (2023), New England J Medicine 388, 1396--1404
Week 7 February 24
- Slides
- Slides (with scribbles)
- EM algorithm from All of Statistics, Chapter 9.13, with typos noted
- Funnel plot article from British Medical Journal
- Handwriting vs typed meta-analysis paper
Week 6 February 10
- Slides
- Slides (with scribbles)
- Balakrishnan & Wasserman multinomial goodness-of-fit tests
- Hore & Barber testing equality of distributions
- Montreal talk (Confidence distributions on first few slides)
- Questions Week 6
Week 5 February 3 (Midterm 1)
- Slides
- Slides (with scribbles)
- Updated syllabus Midterm 2 moved to March 10
- Questions Week 5
- Typed vs handwritten lecture notes -- meta-analysis article from Educational Psychology Review
Week 4 January 27
- Slides
- Slides (with scribbles)
- Questions Week 4
Week 3 January 20
- Slides
- Slides with scribbles
- Updated syllabus
- Questions Week 3
Week 2 January 13
- Slides
- Slides with scribbles
- Extended likelihood cheat-sheet
- Questions Week 2 (typo corrected Jan 19)
Week 1 January 6
- Slides
- Slides with scribbles
- Course Information
- Current Syllabus
- Questions Week 1
- Likelihood cheat-sheet
- Preliminary project information
- R Markdown file for geometric likelihood function
Texts (all on Quercus course page)
- Main
- All of Statistics by L. Wasserman. e-copy here
- Likelihood and its Extensions by Reid, Varin, Yi.
- Statistical Models by A.C. Davison.
- For reference
- Computer Age Statistical Inference by B. Efron and T. Hastie
- Mathematical Statistics by K. Knight
Grading
The grade will be based on two one-hour midterms (Feb 3 and Mar 3), (50%) a final project (40%), and class participation (10%).The project will require reading and reporting on a paper in the statistical literature. The project grade will be based on a presentation in the final class and a written report. A list of potential papers and a grading rubric will be provided.