Module Description
This module introduces methods for analysing response data that are categorical, rather than continuous. Topics include: categorical response data and contingency tables, loglinear and logit models, Poisson regression, framework of generalised linear models, model diagnostics, ordinal data. This module is targeted at students who are interested in Statistics and are able to meet the prerequisite.
Topics
- Two-way Contingency Tables
- Three-way Contingency Tables
- Generalized Linear Models
- Logistic Regression
- Loglinear Models
- Multicategory Logit Models
I found this to be a very informative and helpful module that adds on to your knowledge from ST3131.
Workload
Two two-hours lectures
I was taught by A/P Zhang Jin-Ting. It took awhile for the class to get used to his accent and the way he pronounces certain words like "saturated". All in all, he is a good lecturer, always keen to answer questions and replies very fast to emails.
Lecture was held in the new LT at Stephen Riady. This is also one of the lousiest LT I had ever visited. There's very little space to move around, air-con doesn't work sometimes and it is pretty dirty considering it is so new.
Weekly one-hour tutorial
The tutorials were not very difficult. Usage of R to do some tests and data modelling will be required. These will be taught during lecture.
Textbook is recommended.
As always, it is always better to revise together with the textbook. However, the lecture notes are quite sufficient for revision. So this is up to you.
Lectures were webcasted.
Assessment
60% Final (True and False, Open-ended)
30% Midterm (True and False, Open-ended)
10% Class Participation (Presenting answers for a few questions)
Cheat sheets allowed.
Extra notes to readers: If you want to know more about the modules I have taken, you may refer to this list or the labels on the right! :D
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