Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group learning activities on campus for the second half-year, while keeping an online version available for those students unable to return or those who choose to continue their studies online.
To check the availability of face to face activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Convenor & Lecturer
Thomas Fung
12 WW 6.26
Monday 3 - 5pm
Lecturer
Benoit Liquet-Weiland
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
((Admission to MAppStat or MSc or MScInnovationStat or GradCertAppStat or GradDipAppStat or MDataSc) and ((STAT806 or STAT810 or STAT6110) and STAT6175)) or (admission to MMarScMgt or MConsBiol or GradDipConsBiol and (STAT830(Cr) or STAT8830(Cr))) or (Admission to MBusAnalytics and BUSA8000 and ECON8040))or (Admission to MActPrac and (STAT806 or STAT810 or STAT8310))
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
STAT8111 is co-taught with STAT8111
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Unit description |
Unit description
This unit starts with the classical normal linear regression model. The family of generalized linear models is then introduced, and maximum likelihood estimators are derived. Models for counted responses, binary responses, continuous non-normal responses and categorical responses; and models for correlated responses, both normal and non-normal, and generalised additive models, are studied. All models and methods are illustrated using data sets from disciplines such as biology, actuarial studies and medicine. |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
ASSIGNMENT SUBMISSION: Assignment submission will be online through the iLearn page.
Submit assignments online via the appropriate assignment link on the iLearn page. A personalised cover sheet is not required with online submissions. Read the submission statement carefully before accepting it as there are substantial penalties for making a false declaration.
You may submit as often as required prior to the due date/time. Please note that each submission will completely replace any previous submissions. It is in your interests to make frequent submissions of your partially completed work as insurance against technical or other problems near the submission deadline.
LATE SUBMISSION OF WORK: All assessment tasks must be submitted by the official due date and time. In the case of a late submission for a non-timed assessment (e.g. an assignment), if special consideration has NOT been granted, 20% of the earned mark will be deducted for each 24-hour period (or part thereof) that the submission is late for the first 2 days (including weekends and/or public holidays). For example, if an assignment is submitted 25 hours late, its mark will attract a penalty equal to 40% of the earned mark. After 2 days (including weekends and public holidays) a mark of 0% will be awarded. Timed assessment tasks (e.g. tests, examinations) do not fall under these rules.
FINAL EXAM POLICY: It is Macquarie University policy not to set early examinations for individuals or groups of students. All students are expected to ensure that they are available until the end of the teaching semester, that is, the final day of the official examination period. The only excuse for not sitting an examination at the designated time is because of documented illness or unavoidable disruption. In these special circumstances, you may apply for special consideration via ask.mq.edu.au.
If you receive special consideration for the final exam, a supplementary exam will be scheduled in the interval between the regular exam period and the start of the next session. By making a special consideration application for the final exam you are declaring yourself available for a resit during this supplementary examination period and will not be eligible for a second special consideration approval based on pre-existing commitments. Please ensure you are familiar with the policy prior to submitting an application.
You can check the supplementary exam information page on FSE101 in iLearn (bit.ly/FSESupp) for dates, and approved applicants will receive an individual notification one week prior to the exam with the exact date and time of their supplementary examination.
Name | Weighting | Hurdle | Due |
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Assignment 1 | 20% | No | Week 4 |
Assignment 2 | 20% | No | Week 8 |
Assignment 3 | 20% | No | Week 11 |
Final Examination | 40% | No | Examination Period |
Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 4
Weighting: 20%
The assignment will focus mainly on the material covered in Lecture Weeks 1-3.
Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 8
Weighting: 20%
The assignment will focus mainly on the material covered in Lecture Weeks 4-6.
Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 11
Weighting: 20%
The assignment will focus mainly on the material covered in Lecture Weeks 7-10.
Assessment Type 1: Examination
Indicative Time on Task 2: 2 hours
Due: Examination Period
Weighting: 40%
The Final Examination will be a two-hour, open-booked online exam (plus ten minutes of reading time).
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
Lectures: There is 1 x 2 hr lecture each week.
SGTAs: There is 1 x 1 hr SGTA class each week.
Course notes are available on iLearn, prior to the lecture. SGTA solutions are posted on iLearn.
There is no prescribed text for this unit. The following are useful references:
A comprehensive list of online resources for self-learning R, is given on iLearn.
en.wikipedia.org/wiki/Generalized_linear_model
We will be using R, which is freely downloadable from the CRAN website. We recommend use of the RStudio interface, also freely downloadable.
We will be using iLearn for posting of course notes, assignments, solutions and data sets, and online discussions. You are encouraged to use the forums for discussions on the course material. Remember that if you are confused about something, the chances are that other students are also confused. Everybody benefits from the discussions, and you should not be embarrassed to admit that you do not understand a concept.
Audio recordings of the lectures (Echo) will be available on the iLearn site.
Week |
Topics |
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1 |
The classical normal linear model |
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2 |
Introduction to GLMs: The framework of generalized linear models is introduced, and the theory behind maximum likelihood estimation of the parameters started. |
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3 |
Maximum likelihood estimation of the parameters; Poisson regression for count data |
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4 |
Inference; comparison of models The deviance as a measure of fit; hypothesis testing |
Assignment 1 due |
5 |
Model checking: Definition of residuals in GLMs; checking for violation of model assumptions |
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6 |
Model selection; overdispersion: Selection of models via AIC; the phenomenon of overdispersion; compound Poisson models to overcome it; the negative binomial model for counts |
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7 |
Binary responses: logistic regression |
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Session 2 Break |
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8 |
Logistic regression contd; Zero-inflated models; Generalized additive models |
Assignment 2 due |
9 |
Regression models for ordinal and categorical responses |
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10 |
Correlated data: Models for longitudinal data, and other data structures in which there is clustering or correlation between observations |
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11 |
Correlated data |
Assignment 3 due |
12 |
Correlated data |
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13 |
No lecture |
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