STA 2101H: Methods of Applied Statistics I

Wednesday September 14 to Wednesday December 7 2022
10am -- 1 pm Eastern
SF 3201


==============================================================

Office Hours

Mondays 7-8 pm (online); Wednesdays 4-5 pm (in person: Power Building 9124)

Delivery

Lecture 1, September 14 is online only. Join via the Zoom link on the Quercus page for the course.

From September 21 we are scheduled to meet in SF 3201 in person. 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; you will find an entry for Piazza in the course menu. If you click it, you will be asked to sign up. You can also sign up at this link.

Project Guidelines

  • Handout with information on project structure and marking
  • The Quercus page has some sample projects as well

December 7

November 30

November 23

November 16

November 2

October 26

October 19

October 12

October 5

September 28

September 21

September 14

Before first lecture

Before the first class, I recommend that you
  • Read Chapter 2 Sections 1 - 4 of Faraway's "Linear Models with R"
  • 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 talk I presented at the Joint Statistical Meetings in August.

Required references

Strongly recommended

Background reading

Your undergraduate regression textbook may be helpful, or
  • A Modern Approach to Regression with R, by S. J. Sheather
  • Data Analysis and Graphics using R, by Maindonald and Braun

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 2021.09.0 Build 351 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.

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.

Course Information Sheet

Syllabus