STA 2101H: Methods of Applied Statistics I Fall 2020

Thursday September 10 to Thursday December 3
12pm -- 3 pm Eastern


Office Hours

Monday 4.00-5.30pm, 7.00-8.00pm; Wednesday 9-10.30am EDT (BBCollaborate Course Room)

Comments on Final Homework

Final Homework

Week 12 December 3

Week 11 November 26

Homework 3 due Dec 3

Week 10 November 19

Week 9 November 5

Week 8 October 29

Homework 2 due November 5

Week 7 October 22

October 18

Week 6 October 15

Week 5 October 8

Week 4 October 1

(typo corrected on slide 10 on Oct 14, thanks Chenghui)

September 28

  • Updated syllabus
  • Slides from Weeks 2,3 have been updated to include 1st edition of Faraway's Linear Models with R

Week 3 September 24

September 22

September 21

  • Short note on least squares equations and matrix algebra.

Week 2 September 17

September 11

Article in the Globe and Mail this morning about the "relatively new field known as event attribution science". You read it here first :) Here are screen shots of the article (best I could do).

Week 1 September 10

Course Information Sheet Updated Sep 3

Syllabus (Update 4)


The class will be delivered at the scheduled time (Thursdays, 12-3 pm Toronto time) using BBCollaborate. 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.

The first hour will usually be mainly lecture-style, with breaks for discussion, on the methods listed in the Syllabus. The second hour will be discussion of case studies, usually from current events, with statistical concepts reviewed as needed in that context. The third hour will be a discussion of computational methods and/or problems, questions about the course material and questions about the homework.

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.

Before first lecture

Before the first class, I recommend that you
  • check that you can link to to my course web page, the Quercus page, and to the reference texts (if you don't have a hard copy)
  • signup for Piazza
  • download and install R and RStudio
  • look at the slides for this public lecture I gave in January

Recommended Texts

Statistical Models by A.C. Davison.
Principles of Applied Statistics by D.R. Cox and C.A. Donnelly
If Davison seems a little heavy, you may prefer your undergraduate regression textbook, or
Linear Models with R, and Extending the Linear Model with R, both by J.J. Faraway. Electronic copies are on the Quercus page.
Other helpful references are Data Analysis and Graphics using R, by Maindonald and Braun, and An Introduction to Generalized Linear Models by Dobson.


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 and the free Desktop Version of Rstudio from\#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.

There are many online resources for R and Rstudio. If you are new to R, you could look at Quick-R. Rstudio has some recommendations on their education page. For more experienced users, the Cheatsheets are invaluable.