STA 2212H: Mathematical Statistics II

January 7 to April 1 2025
Tuesday 10.10 am - 1.00 pm Eastern

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Office Hours

Monday 7.00--8.00 pm (Zoom), and
Tuesday 3.00--4.00 pm (Hydro 9124)

Teaching Assistant

Junhao Zhu

Week 12 April 1

• Course summary slides
• Presentation Schedule. Note start at 10.00 sharp.

Week 11 March 25

• Slides
• Slides with scribbles
• funnel plots (Sterne et al., 2011, Br. Med. J.) (meta-analysis)
• ISIS-4 study (RCT related to meta-analysis)
• Obesity RCT (Heerman et al., 2024, JAMA) (multiple imputation)

Week 10 March 18

• Slides
• Slides with scribbles
• Air pollution and causality

Week 9 March 11

• Slides
• Slides with scribbles
• Blog post by Tewari on using conformal prediction
• Great set of notes on conformal prediction by Ryan Tibhsirani

Week 8 March 4

• Slides
• Slides with scribbles
• BMJ article on exercise and depression
• Cochrane review of Covid tests
• PLoS One paper on rapid flow tests

Week 7 February 25

• Slides
• Slides with scribbles
• Paper on domestication of dogs
• Hartigan's Dip Test
• R package `diptest'

Week 6 February 11

• Slides
• Slides with scribbles
• Updated syllabus
• Lancet phone study
• Guardian article
• CI visualization (Thanks Evelyn!)

Week 5 February 4

• Slides
• Slides with scribbles
• Stigler (2025) ``Bayesian issues in the 1950s"

Week 4 January 28

• Slides
• Slides with scribbles
• JAMA paper Hernandez et al., 2019
• Edelman Trust Barometer Canada report (see p.12 re AI)

Week 3 January 21

• Slides
• Slides (with scribbles)
• Guardian news article
• Lancet article

Week 2 January 14

• Slides
• Slides (with scribbles)
• NY Times article
• Alcohol guidelines, Canada
• National Academy Report

Week 1 January 7

• Slides
• Slides with scribbles
• Likelihood cheatsheet (short)
• Project Introduction
• This longer version of the likelihood cheatsheet has the details for proving asymptotic normality
• BMJ article on chocolate and Type 2 diabetes
• NY Times
• Geometric likelihood
  • R script for geometric likelihood
  • R Markdown lets you change parameters easily
  • Output from R Markdown

Course Information

Syllabus

Texts

• Required
  • Mathematical Statistics by K. Knight; Cambridge University Press
    Hard copy available at publisher's website
  • All of Statistics by L. Wasserman. e-copy here or see course page on Quercus
  • Statistical Models by A.C. Davison.
  • See also "Modules" section on Quercus
• For reference
  • Computer Age Statistical Inference by B. Efron and T. Hastie
  • Statistical Inference by G. Casella and R.L. Berger

Grading

The grade will be based on homework (60%) and a final project (40%).
Homework is assigned weekly, due the following week. The best 8 of 10 HW grades will determine the homework portion of your final mark.
The project will require reading and reporting on a paper in the statistical literature. You will work in teams of two. 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.

Computing

I will always refer to the R computing package and I highly recommend the RStudio environment. You will need to install both of these on your laptop. I am using Version 4.1.1 of R, and Version 1.4.1717 of Rstudio. You can download R from https://cran.r-project.org/ and the free Desktop Version of Rstudio from https://rstudio.com/products/rstudio/\#rstudio-desktop.

I also strongly recommend using R Markdown or LateX to prepare your homework. The Overleaf Editor is an easy way to get started with LateX. Neat homework makes it easier on the grader, and a happy grader is a generous grader.

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