(Updated: September 05 2017 09:24)

Doubt is not a pleasant condition, but certainty is absurd. — Voltaire

Course work and grades

  • Final exam during final exam period: 35%
  • Midterm test Friday, October 20: 25%
  • Assignments (team and/or individual) (4): 20%
  • Weekly activity usually in-class on Mondays or due Monday: 10%
    • Varies from week to week. Announced on previous Wednesday if possible.
    • Could be a brief assignment, a team assignment, or an in-class quiz.
    • You must be present to get a grade for an in-class activity.
    • Only the best 10 are counted.
  • Participation: 10% (possibility of bonus marks for outliers)
    • Participate in class and contribute at least 10 meaningful posts on Piazza including at least 5 responses or edits to other posts. Within three days of the last class, you post a brief summary (500 words or less) of the contributions you want considered for participation including links to them.
    • Post a ‘weekly link’, a link to something on the web that is interesting and relevant to statistics. Include a brief description and click on the weekly_link tag when you submit the post. Here’s an example.
    • Find, download and save as a .csv file at least one interesting data set from the web, other than the data sets readily available through R and through the textbook. Post a description on Piazza and send both the data set and a codebook to the instructor so it can be uploaded to the class server.

Prerequisites

The prerequisites for taking this course are MATH3330 and MATH3131 or their equivalent. If you have not already passed both of these courses you see me to seek permission to take MATH4330.

Textbooks and notes

  • John Fox (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition, Sage.
  • Alan Agresti (2007) Introduction to Categorical Data Analysis, Second Edition, Wiley
  • Michael Evans and Jeffrey Rosenthal (2009) Probability and Statistics – The Science of Uncertainty, 2nd ed., available online
  • Notes on R: Some evolving notes on the use of R and RStudio.

Getting help

  • You can post questions on Piazza – and get participation credit for posting questions and replies. I will monitor Piazza and occasionally participate and comment on questions and answers.
  • You can ask your teammates or other classmates directly.
  • You can see the instructor during office hours or after class.
  • You can send me e-mail. If it’s a question of general interest, I might post it on Piazza to invite others to answer and/or to answer myself.

Teams

Many weekly activities and assignments are done in semi-randomly assigned teams that will be assigned on Monday, September 11 using the information you provide in the first weekly activity due Monday at 9 am on September 11. The teams remain the same for the course. Why random teams? One reason is that in almost all job interviews, you are asked about your experience working with teams.

Working with a diverse team that you didn’t select yourself gives you the opportunity to have experiences that will give you great anecdotes to use in your future job interviews.

When you land the job, you will be much more likely to show the kind of leadership in team work that is invaluable in the modern workplace.

General comments and details

I will email the list of members in your team on Monday, September 11. The members of your team can communicate by email, meet in person, and register for the team forum on Piazza.

If not otherwise indicated, assignments are due at 11:59 pm on the due date. For team activities, get a final draft ready by Monday morning so everyone on the team can do a final check.

Include the names of all active participants on the first page of the assignment. Everyone who participated actively gets the same grade. Those who didn’t, get no grade. Note that some team members might not respond because they have dropped – or intend to drop – the course. If your team shrinks to 3 or fewer, let me know and I can merge your team with another small team. If some members of the team consistently do significantly less work than other members, please inform the instructor and grades will be adjusted accordingly.

Half-way through the course and at the end of the course, you will prepare an assessment of your own and your team members’ contributions. You will discuss the assessment with your team and hand it in to the instructor. The instructor will use the assessment to evaluate the progress of team work generally in the class and to identify and address potential issues. If some team members are low outliers in the assessment, the instructor will discuss the issue with the team and grades will be adjusted for team members who consistently contribute less than a reasonable share.

The more work you do on an assignment the better prepared you are to do well on the term test and on the final exam. But you shouldn’t hog the work – let others do their part too. Everyone should make sure that they understand the whole assignment. Discuss the assignment with your team members to make sure everyone understands the key points and difficulties of each question.

Syllabus

Tentative syllabus: The syllabus will be refined as the course progresses.

We will start with an intensive review of linear models followed by a study of generalized linear models using Fox (2016). We then cover other topics in categorical data analysis using Agresti (2007), which is available online through the York library. Evans and Rosenthal (2009), now available online, will be used as a reference for statistical theory.

We will also use a number of other references including in-class lecture slides and journal articles.

Course policies

Late assignments

Late assignments or projects are penalized 20% of the value of the assignment for each day (or portion of a day) they are late. Unless a different time is specified, assignments and projects are due at 11:59 pm on the due date. Teams should plan to have a ‘final draft’ of team assignments prepared before the deadline so every member of the team can review and okay the draft before submission.

Missed term test

If you miss the term test with a suitably documented medical or compassionate reason, your mark for the term test will be imputed from your mark on the final exam. Otherwise you receive a grade of zero for the term test.

Use of computers in class

You should bring your laptop to class to use it for purposes directly related to the class such as taking notes, annotating slides posted on the web or trying out commands in R. Be aware that some pedagogical research suggests that taking handwritten notes leads to deeper learning for many students. I don’t think that this is true for all students and that is one reason why I would not consider requiring students for forego the use of computers.

It is natural to think that you do not affect anyone else if you are doing your own thing in class on your laptop, phone or tablet.
This is unfortunately incorrect. People seated around you cannot help but be distracted. The instructor gets distracted when members of the class are clearly lost in a different dimension. Therefore, you are requested to not use your laptop to view unrelated materials because this creates distractions for other students and the instructor.

Academic honesty

Familiarize yourself with the York University Senate Policy on Academic Honesty. Violations of academic honesty are treated very seriously in university.

Calendar Entry

Course Description:

Categorical response data, two-way and three-way contingency tables, odds ratios, tests of independence, partial association. Generalized linear models. Logistic regression. Poisson regression. Count regression for Rate Data. Multicategory Logit Models. Prerequisite: SC/MATH 3131 3.00; SC/MATH 3330 3.00. Course credit exclusion: SC/MATH 3034 3.00.

If you lack either prerequisite, you must ask the instructor for permission to take the course, otherwise you may be denied credit for the course even if you pass.

Agresti, Alan. 2007. An Introduction to Categorical Data Analysis. Second. John Wiley. http://ebookcentral.proquest.com.ezproxy.library.yorku.ca/lib/york/detail.action?docID=290465.

Evans, Michael J, and Jeffrey S Rosenthal. 2009. Probability and Statistics: The Science of Uncertainty. Second. Macmillan.

Fox, John. 2016. Applied Regression Analysis and Generalized Linear Models. 3rd ed. Sage Publications.