Session 2 Learning and Teaching Update
The decision has been made to conduct study online for the remainder of Session 2 for all units WITHOUT mandatory on-campus learning activities. Exams for Session 2 will also be online where possible to do so.
This is due to the extension of the lockdown orders and to provide certainty around arrangements for the remainder of Session 2. We hope to return to campus beyond Session 2 as soon as it is safe and appropriate to do so.
Some classes/teaching activities cannot be moved online and must be taught on campus. You should already know if you are in one of these classes/teaching activities and your unit convenor will provide you with more information via iLearn. If you want to confirm, see the list of units with mandatory on-campus classes/teaching activities.
Visit the MQ COVID-19 information page for more detail.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Convenor/Lecturer
Thomas Fung
Contact via Email
Room 626, 12 Wally's Walk
See iLearn for details
Lecturer
Benoit Liquet-Weiland
Contact via Email
See iLearn for details
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
((Admission to MAppStat 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
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Unit description |
Unit description
This unit has an online offering for S2 which is synchronous, meaning there will be set times to attend online lectures and tutorials. 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 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: There is no final exam for this unit.
Name | Weighting | Hurdle | Due |
---|---|---|---|
Assignment 1 | 30% | No | Week 4 |
Assignment 2 | 40% | No | Week 9 |
Assignment 3 | 30% | No | Week 13 |
Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 4
Weighting: 30%
Assignment
Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 12 hours
Due: Week 9
Weighting: 40%
Assignment
Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 13
Weighting: 30%
Assignment
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 the use of the RStudio interface, also freely downloadable.
We will be using iLearn for posting 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.
Unit Schedule
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 |
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9 |
No Lecture (Labour Day Public Holiday) |
Assignment 2 due |
10 |
Regression models for ordinal and categorical responses |
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11 |
Correlated data: Models for longitudinal data, and other data structures in which there is clustering or correlation between observations |
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12 |
Correlated data |
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13 |
Correlated data |
Assignment 3 due |
Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.
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Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to help you improve your marks and take control of your study.
The Library provides online and face to face support to help you find and use relevant information resources.
Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
For all student enquiries, visit Student Connect at ask.mq.edu.au
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For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/.
When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.
The final examination has been removed from this unit.
Unit information based on version 2021.06 of the Handbook