Unit convenor and teaching staff |
Unit convenor and teaching staff
Lecturer/Convener
Hassan Doosti
Contact via Email
Room 534, 12 Wally's Walk
Please refer to iLearn for Consultation hours.
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
((STAT6170 or STAT670) and (BCA802 or STAT8602 or MATH604 or MATH6904)) or (Admission to MDataSc and (STAT6170 or STAT670))
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Corequisites |
Corequisites
STAT6180 or STAT680 or STAT6183 or STAT683
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Co-badged status |
Co-badged status
This unit is co-badged with STAT3175.
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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 generalized 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:
Requirements to Pass this Unit
To pass this unit you must:
Assignment Submission
Assignment submission will be online through the iLearn page. Your name and Student ID should appear on the first 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 best interest to make frequent submissions of your partially completed work as insurance against technical or other problems near the submission deadline.
Late Assessment Submission Penalty
Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark of the task) will be applied for each day a written report or presentation assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of ‘0’ will be awarded even if the assessment is submitted. The submission time for all uploaded assessments is 11:55 pm. A 1-hour grace period will be provided to students who experience a technical concern.
For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, please apply for Special Consideration.
Assessments where Late Submissions will be accepted
In this unit, late submissions will accepted as follows:
Special Consideration
The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the assessments in this unit on time, please inform the convenor and submit a Special Consideration request through ask.mq.edu.au.
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 | 15% | No | Week 4 |
Assignment 2 | 15% | No | Week 8 |
Assignment 3 | 15% | No | Week 12 |
Final examination | 55% | No | University Examination Period |
Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 4
Weighting: 15%
Reinforce and apply the concepts covered in lectures and the skills learned in SGTA classes, through data analysis.
Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 8
Weighting: 15%
Reinforce and apply the concepts covered in lectures and the skills learned in SGTA classes, through data analysis.
Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 12
Weighting: 15%
Reinforce and apply the concepts covered in lectures and the skills learned in SGTA classes, through data analysis.
Assessment Type 1: Examination
Indicative Time on Task 2: 23 hours
Due: University Examination Period
Weighting: 55%
Formal invigilated examination testing the learning outcomes of 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
There is one one hour lecture and one two hour SGTA each week. Lectures begin in Week 1 and SGTAs in Week 2. Please consult the timetable for the scheduling of these activities. Attendance at the online SGTA will be kept.
In addition to the one hour lecture there are online resources including videos which should be viewed prior to the one hour lecture.
Technologies used and required
Lecture material will be placed on iLearn. The statistical package R will be used.
SGTA
SGTAs are held online. You practice the techniques learnt in lectures and from the above mentioned online resources. You will complete worksheets as part of the learning process.
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. This is available online.
COVID Information
For the latest information on the University’s response to COVID-19, please refer to the Coronavirus infection page on the Macquarie website: https://www.mq.edu.au/about/coronavirus-faqs. Remember to check this page regularly in case the information and requirements change during semester. If there are any changes to this unit in relation to COVID, these will be communicated via iLearn.
Methods of Communication
We will communicate with you via your university email or through announcements on iLearn. Queries to the convenor can either be placed on the iLearn discussion board or sent to hassan.doosti@mq.edu.au from your university email address.
Week | Topic/s |
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1 | Simple linear regression. Multiple linear regression. |
2 | The model in matrix form. Diagnostics. |
3 | Diagnostics. Transformations. |
4 | Transformations. Collinearity. |
5 | Polynomial regression. Categorical covariates. |
6 | Analysis of change. Analysis of covariance (ANCOVA). |
7 | Confounding. Interaction. |
8 | Variable selection. Model building. |
TWO WEEK BREAK | |
9 | Introduction to generalized linear models. Logistic regression. |
10 | Logistic regression. Poisson regression. |
11 | Poisson regression. Negative binomial regression. |
12 | Negative binomial regression. Gamma regression. |
13 | Revision. |
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We value student feedback to be able to continually improve the way we offer our units. As such we encourage students to provide constructive feedback via student surveys, to the teaching staff directly, or via the FSE Student Experience & Feedback link in the iLearn page. Student feedback from the previous offering of this unit was very positive overall, with students pleased with the clarity around assessment requirements and the level of support from teaching staff. As such, no change to the delivery of the unit is planned, however we will continue to strive to improve the level of support and the level of student engagement.
Unit information based on version 2024.01R of the Handbook