Personnel

Class meetings

General info

Scope

The course will cover the basics of linear and (mostly) generalized linear models; I will assume familiarity with ANOVA and linear regression. While the theoretical framework will be presented, the emphasis will be on solving practical problems. We will go into detail on preparing data for analysis; deciding on and implementing an appropriate models; diagnosing model fit; and interpreting results. Some important statistical topics that I feel are insufficiently covered elsewhere in the curriculum (snooping/garden of forking paths, ethics, data wrangling) will be covered in passing.

Assignments and assessment

The assignments for the course will consist of biweekly problem sets (a mixture of computational (R-based) and analytical work) (25%), midterm exam (take-home: 25%), a project/extended data analysis (10%) and a take-home final exam (35%). Class participation (see below) will count for 5%.

Grades will be posted on Avenue.

The due dates for assignments are on the course schedule. Assignments are to be handed in before 11:59 PM on the day they are due. Your submission must be in the form of a plain-text file (.R, .Rnw, or .Rmd) along with graphs stored as PDFs and any data files that are necessary to make it run. It must be reproducible on my and the TA’s computer. - There will be a 10% per day late penalty. - An example of a homework assignment in acceptable format is given under the HomeworkExample directory on the course repository.

Participation marks

Although you may have experienced otherwise in other courses, these marks will not be arbitrary/subjective. Occasionally throughout the semester, you will be asked to write either the instructor or your TA an email (a few sentences to a paragraph) on a particular topic (subject 4/6C03 participation). The purpose of these emails is for us to be able to assess your depth of understanding of non-technical material. This will allow us to give you feedback and will hopefully make the preparation of your final report at the end of the term less stressful. If you do not come to lecture you will not hear about these participatory emails. If you do not send these emails you will receive a low participation mark. You can also enhance your participation mark by engaging with in-class exercises and coming to office hours.

You can gain your first participation mark by sending an e-mail to the instructor or the TA whose subject line is “stats 4/6C03: <your_macid> read the outline”, where <your_macid> is your MacID (not your student number!)