Unit convenor and teaching staff |
Unit convenor and teaching staff
Convenor
Gillian Heller
12 WW 7.25
Tuesday 10 - 12
Lecturer
Thomas Fung
12 WW 6.26
Tuesday 3 - 5pm
Frank Schoenig
Thomas Fung
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Credit points |
Credit points
4
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Prerequisites |
Prerequisites
(Admission to MAppStat or MSc or GradCertAppStat or GradDipAppStat or MActPrac or MDataSc or MScInnovation and (STAT806 or STAT810)) or (admission to MMarScMgt or MConsBiol or GradDipConsBiol and STAT830(Cr))
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
STAT811 is co-taught with STAT711
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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. Zero-inflated models are also considered. All models and methods are illustrated using data sets from disciplines such as biology, actuarial studies and medicine.
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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, by the given due date and time.
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.
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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 assignments or assessments must be submitted by the official due date and time. No marks will be given to late work unless an extension has been granted following a successful application for Special Consideration. Please contact the unit convenor for advice as soon as you become aware that you may have difficulty meeting any of the assignment deadlines. It is in your interests to make frequent submissions of your partially completed work. Note that later submissions completely replace any earlier submission, and so only the final submission made before the due date will be marked.
Examination There will be a two-hour sit-down examination. You will be permitted to bring an A4 sheet of notes, handwritten or typed, on both sides, into the examination. The sit-down examination will be timetabled in the official University examination timetable.
FINAL EXAM POLICY: 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.
SUPPLEMENTARY EXAMINATIONS:
IMPORTANT: 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. If you apply for special consideration, you must give the supplementary examination priority over any other pre-existing commitments, as such commitments will not usually be considered an acceptable basis for a second application for special consideration. 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 (https://bit.ly/FSESupp) for dates, and approved applicants will receive an individual notification sometime in the 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 | 19 August |
Assignment 2 | 20% | No | 30 September |
Assignment 3 | 20% | No | 28 October |
Final examination | 40% | No | Examination period |
Due: 19 August
Weighting: 20%
Due: 30 September
Weighting: 20%
Due: 28 October
Weighting: 20%
Due: Examination period
Weighting: 40%
Lectures and SGTAs are at the following times:
Lecture: Tuesday 6-8pm, Tuesday 6-8pm, 9 Wally's Walk (E6A) - 133 Tutorial Rm
SGTA: Tuesday 8-9pm, 6 Eastern Rd (E4B) - 118 Faculty PC Lab
SGTAs run from week 1 to week 12.
External students are expected to study the course notes and attempt the SGTAs, weekly. They are also welcome to optionally attend the weekly lectures and SGTAs.
Course notes: 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 |
Assn 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 |
Assn 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 |
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12 |
Correlated data |
Assn 3 due |
13 |
No lecture |
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Date | Description |
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30/07/2019 | Lecture venue changed to accommodate larger numbers. |