Students
STAT726 – Market Research and Forecasting
2018 – S2 Day
General Information
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| Unit convenor and teaching staff |
Unit convenor and teaching staff
Tania Prvan
12 Wally's Walk, Room 629
TBA
Tutor
Karol Binkowski
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|---|---|
| Credit points |
Credit points
4
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| Prerequisites |
Prerequisites
Admission to MRes
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| Corequisites |
Corequisites
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| Co-badged status |
Co-badged status
Co-taught with STAT328 and co-badged with STAT826.
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| Unit description |
Unit description
Advanced quantitative methods including multivariate and other statistical methods that have important applications in market research will form the first part of this unit. Consumer choice modelling (conjoint analysis) will also be discussed. Methods for modelling and forecasting trends based on time series data, including techniques for seasonal adjustment will be covered. The unit will make extensive use of appropriate computer packages including SPSS.
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Important Academic Dates
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
Learning Outcomes
On successful completion of this unit, you will be able to:
- Be able to perform an appropriate principal components analysis (PCA) and interpret the results.
- Be able to perform an appropriate Factor Analysis (FA) and interpret the results.
- Be able to perform a Conjoint Analysis (CA) and generate an orthogonal plan.
- Be able to fit an appropriate AR, MA or ARIMA time series model to data and interpret the results.
- Be able to fit an appropriate ARIMA with regressors to data and interpret the results.
- Be able to smooth data and fit an appropriate ARIMA to the smoothed data.
General Assessment Information
In the case of a late submission for an assignment, if no special consideration has been granted, 10% of the earned mark will be deducted for each day that an assignment is late, up to a maximum of 50%. After 5 days, counted including weekends and public holidays, a mark of 0% will be awarded. NOTE: It is not the intention of this late penalty policy to cause a student to fail the unit when they have submitted their assignment no more than 5 days after the due date and they would have otherwise passed. In this case, if deductions for late assignments result in the final unit mark for a student being less than 50 out of 100, when otherwise it would have been 50 out of 100 or greater, the student's final mark will be exactly 50 out of 100.
Assessment Tasks
| Name | Weighting | Hurdle | Due |
|---|---|---|---|
| Assignment 1 | 10% | No | 31/8/2018 |
| Assignment 2 | 10% | No | 26/10/2018 |
| Class Test 1 | 15% | No | Week 6 Lecture |
| Class Test 2 | 10% | No | Week 12 Lecture |
| HW and Tutorial Participation | 5% | No | Weekly |
| Final Examination | 50% | No | TBA |
Assignment 1
Due: 31/8/2018
Weighting: 10%
Assignment 1 must be word processed and submitted in pdf format online via iLearn by 2pm on the due date. There is no "group work" assessment in this unit. All work is to be the student's own.
Please refer to General Assessment Information for lateness penalty.
On successful completion you will be able to:
- Be able to perform an appropriate principal components analysis (PCA) and interpret the results.
- Be able to perform an appropriate Factor Analysis (FA) and interpret the results.
Assignment 2
Due: 26/10/2018
Weighting: 10%
Assignment 1 must be submitted in pdf format online via iLearn by 2pm on the due date. This can be done in Word. There is no "group work" assessment in this unit. All work is to be the student's own.
Please refer to General Assessment Information for lateness penalty.
On successful completion you will be able to:
- Be able to perform a Conjoint Analysis (CA) and generate an orthogonal plan.
- Be able to fit an appropriate AR, MA or ARIMA time series model to data and interpret the results.
- Be able to fit an appropriate ARIMA with regressors to data and interpret the results.
- Be able to smooth data and fit an appropriate ARIMA to the smoothed data.
Class Test 1
Due: Week 6 Lecture
Weighting: 15%
This will be held in the week 6 lecture.
Permitted materials for the class test are a calculator, lecture notes, assignments, assignment solutions, homework solutions, and tutorial solutions. There is no computer access during the class test. No electronic devices are allowed (e.g. iPhones, iPads, tablets, laptops, mobile phones) apart from nonprogrammable calculators.
Failure to attend the test without relevant documentation to explain the absence (submitted as a Special Consideration request within 5 working days of the test) will result in zero marks being awarded for the test.
The procedure for submitting a Special Consideration online can be obtained by visiting ask.mq.edu.au.
Class Test 1 will be of 50 minutes duration.
On successful completion you will be able to:
- Be able to perform an appropriate principal components analysis (PCA) and interpret the results.
- Be able to perform an appropriate Factor Analysis (FA) and interpret the results.
- Be able to perform a Conjoint Analysis (CA) and generate an orthogonal plan.
Class Test 2
Due: Week 12 Lecture
Weighting: 10%
This will be held in the week 12 lecture.
Permitted materials for the class test are a calculator, lecture notes, assignments, assignment solutions, homework solutions, and tutorial solutions. There is no computer access during the class test. No electronic devices are allowed (e.g. iPhones, iPads, tablets, laptops, mobile phones) apart from nonprogrammable calculators.
Failure to attend the test without relevant documentation to explain the absence (submitted as a Special Consideration request within 5 working days of the test) will result in zero marks being awarded for the test.
The procedure for submitting a Special Consideration online can be obtained by visiting ask.mq.edu.au.
Class Test 2 will be of 50 minutes duration.
On successful completion you will be able to:
- Be able to fit an appropriate AR, MA or ARIMA time series model to data and interpret the results.
- Be able to fit an appropriate ARIMA with regressors to data and interpret the results.
HW and Tutorial Participation
Due: Weekly
Weighting: 5%
Every week tutorial participation will be monitored and most weeks there will be set homework to submit on iLearn.
On successful completion you will be able to:
- Be able to perform an appropriate principal components analysis (PCA) and interpret the results.
- Be able to perform an appropriate Factor Analysis (FA) and interpret the results.
- Be able to perform a Conjoint Analysis (CA) and generate an orthogonal plan.
- Be able to fit an appropriate AR, MA or ARIMA time series model to data and interpret the results.
- Be able to fit an appropriate ARIMA with regressors to data and interpret the results.
- Be able to smooth data and fit an appropriate ARIMA to the smoothed data.
Final Examination
Due: TBA
Weighting: 50%
The final examination will be held during the end-of-year Examination period. The final examination is 3 hours long (with an additional 10 minutes’ reading time). It will cover all topics in the unit. The final examination is closed book. Students may take into the final Exam ONE A4 pages of notes handwritten (not typed) on BOTH sides. Calculators will be needed but must not be of the text/programmable type.
The University Examination timetable will be available in Draft form approximately 8 weeks before the commencement of the examinations and in Final form approximately 4 weeks before the commencement of the examinations.
The only exception to not sitting an examination on the designated date is because of documented illness or unavoidable disruption. In these circumstances you may wish to consider applying for special consideration.
If your special consideration for your final examination is approved, you must make yourself available for the week of 17 December – 21 December, 2018. If you are not available at that time, there is no guarantee an additional examination time will be offered. Specific examination dates and times will be determined at a later date.until the end of the teaching semester, that is, the final day of the official examination period.
It is Macquarie University policy to not set early examinations for individuals or groups of students. All students are expected to ensure that they are available.
On successful completion you will be able to:
- Be able to perform an appropriate principal components analysis (PCA) and interpret the results.
- Be able to perform an appropriate Factor Analysis (FA) and interpret the results.
- Be able to perform a Conjoint Analysis (CA) and generate an orthogonal plan.
- Be able to fit an appropriate AR, MA or ARIMA time series model to data and interpret the results.
- Be able to fit an appropriate ARIMA with regressors to data and interpret the results.
- Be able to smooth data and fit an appropriate ARIMA to the smoothed data.
Delivery and Resources
Every week there is one two hour lecture and one two hour practical in this unit. Lectures and Practicals commence in Week 1.
Technologies used and required: Lecture material will be placed on iLearn. The statistical package SPSS will be used and is available in iLab.
Recommended Texts: There is no set textbook for this unit. Useful reference texts for the Market Research part of this unit are
- Applied Multivariate Techniques by Subhash Sharma (QA278.S485/1996)
- Applied Multivariate Methods for Data Analysis by Dallas E. Johnson (QA278.J615/1998)
- Multivariate Statistical Methods by Bryan F. J. Manly (QA278.M35/2004)
There is no suitable text for Conjoint Analysis. Most treatments in Market Research textbooks are either too simple or too technical. A useful reference for the Forecasting part is
- Forecasting: Methods and Applications by Spyros Makridakis, Steven C. Wheelwright and Rob J. Hyndman (HD30.27.M34/1998)
Unit Schedule
| Week | Topic |
|---|---|
| 1 (30 July) | Principal Component Analysis (PCA) |
| 2 (6 August) | PCA |
| 3 (13 August) | Factor Analysis (FA) |
| 4 (20 August) | FA |
| 5 (27 August) | Conjoint Analysis (CA) |
| 6 (3 September) | Revision and Class Test 1 |
| 7 (10 September) | Introduction to Forecasting |
| 8 (2 October) | ARIMA models |
| 9 (8 October) | ARIMA models |
| 10 (15 October) | Dynamic Regression models and intervention analysis |
| 11 (22 October) | Exponential smoothing and Periodicity |
| 12 (29 October) | Revision and Class Test 2 |
| 13 (5 November) | Revision |
Policies and Procedures
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:
- Academic Appeals Policy
- Academic Integrity Policy
- Academic Progression Policy
- Assessment Policy
- Fitness to Practice Procedure
- Grade Appeal Policy
- Complaint Management Procedure for Students and Members of the Public
- Special Consideration Policy (Note: The Special Consideration Policy is effective from 4 December 2017 and replaces the Disruption to Studies Policy.)
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).
Student Code of Conduct
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
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.
Student Support
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Learning Skills
Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to improve your marks and take control of your study.
Student Services and Support
Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
Student Enquiries
For all student enquiries, visit Student Connect at ask.mq.edu.au
IT Help
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.
Graduate Capabilities
PG - Capable of Professional and Personal Judgment and Initiative
Our postgraduates will demonstrate a high standard of discernment and common sense in their professional and personal judgment. They will have the ability to make informed choices and decisions that reflect both the nature of their professional work and their personal perspectives.
This graduate capability is supported by:
Learning outcomes
- Be able to perform an appropriate principal components analysis (PCA) and interpret the results.
- Be able to perform an appropriate Factor Analysis (FA) and interpret the results.
- Be able to perform a Conjoint Analysis (CA) and generate an orthogonal plan.
- Be able to fit an appropriate AR, MA or ARIMA time series model to data and interpret the results.
- Be able to fit an appropriate ARIMA with regressors to data and interpret the results.
- Be able to smooth data and fit an appropriate ARIMA to the smoothed data.
Assessment tasks
- Assignment 1
- Assignment 2
- Class Test 1
- Class Test 2
- HW and Tutorial Participation
- Final Examination
PG - Discipline Knowledge and Skills
Our postgraduates will be able to demonstrate a significantly enhanced depth and breadth of knowledge, scholarly understanding, and specific subject content knowledge in their chosen fields.
This graduate capability is supported by:
Learning outcomes
- Be able to perform an appropriate principal components analysis (PCA) and interpret the results.
- Be able to perform an appropriate Factor Analysis (FA) and interpret the results.
- Be able to perform a Conjoint Analysis (CA) and generate an orthogonal plan.
- Be able to fit an appropriate AR, MA or ARIMA time series model to data and interpret the results.
- Be able to fit an appropriate ARIMA with regressors to data and interpret the results.
- Be able to smooth data and fit an appropriate ARIMA to the smoothed data.
Assessment tasks
- Assignment 1
- Assignment 2
- Class Test 1
- Class Test 2
- HW and Tutorial Participation
- Final Examination
PG - Critical, Analytical and Integrative Thinking
Our postgraduates will be capable of utilising and reflecting on prior knowledge and experience, of applying higher level critical thinking skills, and of integrating and synthesising learning and knowledge from a range of sources and environments. A characteristic of this form of thinking is the generation of new, professionally oriented knowledge through personal or group-based critique of practice and theory.
This graduate capability is supported by:
Learning outcomes
- Be able to perform an appropriate principal components analysis (PCA) and interpret the results.
- Be able to perform an appropriate Factor Analysis (FA) and interpret the results.
- Be able to perform a Conjoint Analysis (CA) and generate an orthogonal plan.
- Be able to fit an appropriate AR, MA or ARIMA time series model to data and interpret the results.
- Be able to fit an appropriate ARIMA with regressors to data and interpret the results.
- Be able to smooth data and fit an appropriate ARIMA to the smoothed data.
Assessment tasks
- Assignment 1
- Assignment 2
- Class Test 1
- Class Test 2
- HW and Tutorial Participation
- Final Examination
PG - Research and Problem Solving Capability
Our postgraduates will be capable of systematic enquiry; able to use research skills to create new knowledge that can be applied to real world issues, or contribute to a field of study or practice to enhance society. They will be capable of creative questioning, problem finding and problem solving.
This graduate capability is supported by:
Learning outcomes
- Be able to perform an appropriate principal components analysis (PCA) and interpret the results.
- Be able to perform an appropriate Factor Analysis (FA) and interpret the results.
- Be able to perform a Conjoint Analysis (CA) and generate an orthogonal plan.
- Be able to fit an appropriate AR, MA or ARIMA time series model to data and interpret the results.
- Be able to fit an appropriate ARIMA with regressors to data and interpret the results.
- Be able to smooth data and fit an appropriate ARIMA to the smoothed data.
Assessment tasks
- Assignment 1
- Assignment 2
- Class Test 1
- Class Test 2
- HW and Tutorial Participation
- Final Examination
PG - Effective Communication
Our postgraduates will be able to communicate effectively and convey their views to different social, cultural, and professional audiences. They will be able to use a variety of technologically supported media to communicate with empathy using a range of written, spoken or visual formats.
This graduate capability is supported by:
Learning outcomes
- Be able to perform an appropriate principal components analysis (PCA) and interpret the results.
- Be able to perform an appropriate Factor Analysis (FA) and interpret the results.
- Be able to perform a Conjoint Analysis (CA) and generate an orthogonal plan.
- Be able to fit an appropriate AR, MA or ARIMA time series model to data and interpret the results.
- Be able to fit an appropriate ARIMA with regressors to data and interpret the results.
- Be able to smooth data and fit an appropriate ARIMA to the smoothed data.
Assessment tasks
- Assignment 1
- Assignment 2
- Class Test 1
- Class Test 2
- HW and Tutorial Participation
- Final Examination