STA 2212H: Mathematical Statistics II

January 13 to April 12
Wednesday 10.10 am - 12.00 pm; Friday 10.10 - 11.00 am; Eastern


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Office Hours

Monday 7.00-8.00 pm; Thursday 11.00 am - 12.00 pm (BBCollaborate Course Room)
Friday 11.00am - 12.00pm (Zoom)

Week 12 April 7-9

Week 11 March 31

Week 10 March 24-26

Week 9 March 17-19

Week 8 March 10-12

Week 7 March 3-5

Week 6 February 24-26

Week 5 February 10

Week 4 February 3/5

Week 3 January 27/29

Week 2 January 20/22

Week 1 January 13/15

Course Information Sheet

Syllabus (updated Mar 2)

Delivery

The class will be delivered at the scheduled time (Wednesday, 10-12; Friday, 10-11 am Toronto time) using Zoom. There is a Zoom link on the Quercus page. The lectures will be recorded for viewing offline after the scheduled time. The slides for the lectures will be posted, on good weeks before the scheduled course time, and on rushed weeks just after.

We will use Piazza for discussion, as it is now integrated with Quercus. You will find an entry for Piazza in the course menu. If you click it, you will be asked to sign up. Please see the instructions in the handout, especially the highlighted bits.

Texts

  • Required: All of Statistics by L. Wasserman.
  • Recommended: Statistical Models by A.C. Davison.
  • Interesting: Computer Age Statistical Inference by B. Efron and T. Hastie
  • Helpful: Mathematical Statistics by K. Knight.
  • Helpful: Statistical Inference by G. Casella and R.L. Berger

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 3.6.3 of R, although Version 4 was released in April 2020. I am using Version 1.3.1073 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 to prepare your homework, but you can use LateX or Word if you must. For questions involving computing you will need to submit working code. This is easy in R Markdown, but R scripts will also be accepted. Neat homework makes it easier on the grader, and a happy grader is a generous grader.