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
Nino Kordzakhia
Contact via E-mail
Please refer to iLearn
Lecturer
Benoit Liquet-Weiland
Contact via Email
Please refer to iLearn
Benoit Liquet-Weiland
|
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Credit points |
Credit points
10
|
Prerequisites |
Prerequisites
STAT6170 or STAT670
|
Corequisites |
Corequisites
STAT6180 or STAT680
|
Co-badged status |
Co-badged status
|
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 introduces statistical tools for multivariate data analysis such as statistical graphics, discriminant analysis, principal component analysis, cluster analysis and an introduction to data mining, especially classification. Statistical packages are used extensively to illustrate the concepts. |
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:
WORK SUBMISSION: The submission link will be available on the iLearn site of the Unit.
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. SGTA work), 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) do not fall under these rules.
Name | Weighting | Hurdle | Due |
---|---|---|---|
SGTA Works | 10% | No | Week 3, 5, 7 and 10 |
Mid-Semester test | 30% | No | Week 8 |
Practical Test | 60% | No | Week 12 |
Assessment Type 1: Qualitative analysis task
Indicative Time on Task 2: 40 hours
Due: Week 3, 5, 7 and 10
Weighting: 10%
The tasks given during four SGTA computer lab sessions are to be completed within the allocated time and submitted via iLearn. The four SGTA Works are worth 10% in total.
Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 1 hours
Due: Week 8
Weighting: 30%
Further information for this online test will be provided in the iLearn site of the unit.
Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 2 hours
Due: Week 12
Weighting: 60%
This is an open book style online exam. The practical test is designed to examine the use of software for data analysis and the software output interpretation skills taught in the unit.
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
Software: SPSS and R
The recommended references are
Chambers J M et al (1983) Graphical Methods for Data Analysis;
Cleveland W S (1994) Elements of Graphing Data;
Tufte E R (2001) The Visual Display of Quantitative Information;
Everitt B S et al (2001) Applied multivariate data analysis;
Johnson, R.A. & Wichern, D.W. (2002) Applied Multivariate Statistical Analysis;
Manly, B F J (2004) Multivariate Statistical Methods - A Primer.
Week |
Topic |
Due |
---|---|---|
1 |
Introduction |
|
2 |
Different graphical displays |
|
3 |
Displaying multivariate data |
SGTA Work |
4 |
Similarities and distances |
|
5 |
Hierarchical cluster analysis |
SGTA Work |
6 |
K-means clustering |
|
7 |
Eigenvalues and eigenvectors |
SGTA Work |
8 |
Principal component analysis |
Mid-Semester Test |
9 |
Principal component analysis cont. |
|
10 |
Discriminant analysis |
SGTA Work |
11 |
Classification Trees Revision |
|
12 |
Final assessment: |
Practical Test |
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.
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Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) 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 or if you are a Global MBA student contact globalmba.support@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 help you improve your marks and take control of your study.
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Unit information based on version 2021.03 of the Handbook