Unit convenor and teaching staff |
Unit convenor and teaching staff
Unit Convenor
Gillian Heller
Contact via gillian.heller@mq.edu.au
Room 6.19, 12 Wally's Walk
TBA
Hassan Doosti
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Credit points |
Credit points
3
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Prerequisites |
Prerequisites
6cp at 200 level including (STAT270 or STAT271 or BIOL235(P) or PSY222 or PSY248(P))
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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 discusses statistical modelling in general and in particular demonstrates the wide applicability of linear and generalised linear models. Topics include multiple linear regression, logistic regression and Poisson regression. The emphasis is on practical issues in data analysis with some reference to the theoretical background. Statistical packages are used for both model fitting and diagnostic testing.
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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:
The only exception for not handing in an assignment on time is documented illness or unavoidable disruption. In these special circumstances you may apply for special consideration via ask.mq.edu.au.
In the case of the late submission of an assignment, if no special consideration has been granted, 10% of the earned mark will be deducted for each day that the assignment is late, up to a maximum of 50%. After 5 days, including weekends and public holidays, a mark of 0% will be awarded for the assignment.
The only exception for not sitting an examination at the designated time is 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 the 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.
The University Examination timetable will be available in draft form approximately eight weeks before the commencement of the examinations and in final form approximately four weeks before the commencement of the examinations at:http://www.timetables.mq.edu.au/
Name | Weighting | Hurdle | Due |
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Test of prerequisite knowledge | 0% | No | 23 March |
Assignment 1 | 15% | No | 20 March |
Assignment 2 | 15% | No | 1 May |
Assignment 3 | 15% | No | 29 May |
Tutorials | 5% | No | - |
Examination | 50% | No | TBA |
Due: 23 March
Weighting: 0%
This quiz is available from Week 1, and is intended as a check on your assumed level of knowledge of linear models. If you do not score well, you are advised to consider withdrawal from the unit before the census date (26 March).
Due: 20 March
Weighting: 15%
There are three assignments, worth 15% each. They should be submitted online, by the due time and date. They give you an opportunity to reinforce and apply the concepts covered in lectures and the skills learned in tutorial sessions.
Due: 1 May
Weighting: 15%
As Assignment 1.
Due: 29 May
Weighting: 15%
As Assignment 1.
Due: -
Weighting: 5%
A mark worth 5% of your final mark, will be given for your participation in the laboratory tutorials, on the basis of collected laboratory sheets.
Due: TBA
Weighting: 50%
The examination will cover the material studied in the whole unit and address all the unit outcomes. You may take one A4 sheet, handwritten on both sides, into the final examination.
You should attend the following classes each week:
· 2 hour lecture: Wednesday 9 - 11am, 12 Second Way (C5A), room 315
· 2 hour laboratory tutorial: Wednesday 11am - 1pm, 6 Eastern Rd (E4B), room 104 OR Friday 2-4pm, 6 Eastern Rd (E4B), room 306
Lectures begin in Week 1. Lecture notes are available on iLearn prior to the lecture.
Tutorials begin in week 1 and are based on work from the current week’s lecture. Tutorials are held in computing labs and allow you to practise techniques learnt in lectures. We will mainly use SPSS, but we will supplement this with other statistical software. You will complete worksheets as part of the learning process. SPSS is installed in the computing labs in E4B, and will be used in tutorial sessions and for assignments. Assignments may be completed in these rooms. It is most convenient to bring a memory stick when using these computers.
Text book The recommended text (available from the Co-op Bookshop) is: Chatterjee S & Hadi AS (2012). Regression Analysis By Example, 5th Revised edition, Wiley.
Calculator You will need a calculator with statistical mode for the final examination.
Software The statistical software SPSS will be the main package used. In addition, we will be demonstrating applications using other statistical software such as Minitab and Arc. All of this software is available in the computer labs in E4B.
Staff consultation hours Members of the Statistics Department have consultation hours each week when they are available to help students. These consultation hours are available on iLearn.
Week |
Topic |
Text chapter |
Assessment |
1 |
Simple linear regression, introduction to multiple linear regression |
1, 2 |
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2 |
The model in matrix form, hypothesis tests, residuals, residual & partial regression plots |
3,4 |
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3 |
Diagnostics contd: extreme observations (leverage, DFBETAs, Cook’s distances); transformations |
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4 |
Transformations contd; collinearity |
4, 6 |
Assignment 1 due |
5 |
Polynomial regression; categorical covariates |
6, 9 |
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6 |
Analysis of change |
5 |
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7 |
Interaction and confounding |
- |
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Mid-semester break |
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8 |
Variable selection, model building |
5 |
Assignment 2 due |
9 |
Introduction to generalized linear models; Logistic regression |
11 |
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10 |
Logistic regression ; Poisson regression |
12 |
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11 |
Poisson, negative binomial regression |
12, 13 |
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12 |
Gamma regression |
13 |
Assignment 3 due |
13 |
Revision |
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Macquarie University policies and procedures are accessible from Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Undergraduate students seeking more policy resources can visit the Student Policy Gateway (https://students.mq.edu.au/support/study/student-policy-gateway). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
If you would like to see all the policies relevant to Learning and Teaching visit Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central).
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/study/getting-started/student-conduct
Results shown in iLearn, or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au.
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 improve your marks and take control of your study.
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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