Students

STAT3128 – Market Research and Forecasting

2023 – Session 2, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor, Lecturer
Dr Tania Prvan
tania.prvan@mq.edu.au
Contact via tania.prvan@mq.edu.au
12 Wally's Walk, Level 6, Room 629
See iLearn for Consultation hours.
Teaching staff
Balamehala Pasupathy
balamehala.pasupathy@mq.edu.au
Contact via balamehala.pasupathy@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
20cp at 2000 level including STAT270 or STAT2170 or STAT271 or STAT2371 or BIOL235(P) or BIOL2610 or PSY222 or PSY248(P) or PSYU2248
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description
Advanced quantitative methods including conjoint analysis, principal component analysis and other statistical techniques that have important applications in market research form the first part of this unit. Emphasis is placed on market research applications. The unit then covers methods for modelling and forecasting trends based on time series data, including procedures for seasonal adjustment.

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:

  • ULO1: perform an appropriate principal components analysis (PCA) and interpret the results.
  • ULO2: perform an appropriate Factor Analysis (FA) and interpret the results.
  • ULO3: perform a Conjoint Analysis (CA) and generate an orthogonal plan.
  • ULO4: fit an appropriate AR, MA or ARIMA time series model to data and interpret the results.
  • ULO5: fit an appropriate ARIMA with regressors to data and interpret the results.
  • ULO6: smooth data and fit an appropriate ARIMA to the smoothed data.

General Assessment Information

There is no "group work" assessment in this unit.

ASSIGNMENT SUBMISSION

Assignment submission will be online through the iLearn page.

Submit the assignment 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. The assignment should be word processed.

  • Assignment submission is via iLearn. You should upload this as a single PDF file.
  • Please note the quick guide on how to upload your assignments provided on the iLearn page.
  • Please make sure that each page in your uploaded assignment corresponds to only one A4 page (do not upload an A3 page worth of content as an A4 page in landscape). If you are using an app like Clear Scanner, please make sure that the photos you are using are clear and shadow-free.
  • It is your responsibility to make sure your assignment submission is legible.
  • If there are technical obstructions to your submission online, please email us to let us know.

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 Assessment Submission Penalty

From 1 July 2022, Students enrolled in Session based units with written assessments will have the following university standard late penalty applied.

Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7 th day (including weekends). After the 7th day, a grade of '0' will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11:55 pm. A 1-hour grace period is 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, students need to submit an application for Special Consideration.

Assessments where Late Submissions will be accepted

In this unit, late submissions will accepted as follows:

  • Assignment - YES, Standard Late Penalty applies
  • Class Test 1 - NO, unless Special Consideration is Granted
  • Class Test 2 - NO, unless Special Consideration is Granted
  • Final Examination - NO, unless Special Consideration is Granted

Requirements to Pass this Unit

  • Achieve a total mark equal to or greater than 50%

Assessment Tasks

Name Weighting Hurdle Due
Class Test 1 15% No Week 6 Lecture
Assignment 10% No Week 9
Class Test 2 15% No Week 12 Lecture
Final Examination 60% No Formal Examination Period

Class Test 1

Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 10 hours
Due: Week 6 Lecture
Weighting: 15%

 

Test

 
On successful completion you will be able to:
  • perform an appropriate Factor Analysis (FA) and interpret the results.
  • perform a Conjoint Analysis (CA) and generate an orthogonal plan.

Assignment

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 9
Weighting: 10%

 

Reinforce and apply the concepts covered in lectures and the skills learned in SGTA sessions, through data analysis.

 
On successful completion you will be able to:
  • perform an appropriate principal components analysis (PCA) and interpret the results.
  • perform an appropriate Factor Analysis (FA) and interpret the results.
  • perform a Conjoint Analysis (CA) and generate an orthogonal plan.
  • fit an appropriate AR, MA or ARIMA time series model to data and interpret the results.
  • fit an appropriate ARIMA with regressors to data and interpret the results.

Class Test 2

Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 10 hours
Due: Week 12 Lecture
Weighting: 15%

 

Test

 
On successful completion you will be able to:
  • fit an appropriate AR, MA or ARIMA time series model to data and interpret the results.
  • fit an appropriate ARIMA with regressors to data and interpret the results.
  • smooth data and fit an appropriate ARIMA to the smoothed data.

Final Examination

Assessment Type 1: Examination
Indicative Time on Task 2: 20 hours
Due: Formal Examination Period
Weighting: 60%

 

Formal invigilated examination testing the learning outcomes of the unit.

 
On successful completion you will be able to:
  • perform an appropriate principal components analysis (PCA) and interpret the results.
  • perform an appropriate Factor Analysis (FA) and interpret the results.
  • perform a Conjoint Analysis (CA) and generate an orthogonal plan.
  • fit an appropriate AR, MA or ARIMA time series model to data and interpret the results.
  • fit an appropriate ARIMA with regressors to data and interpret the results.
  • smooth data and fit an appropriate ARIMA to the smoothed data.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Delivery and Resources

Classes

Lectures (commencing Week 1): There is one one-hour lecture each week. In addition to the one hour lecture there are online resources including videos which should be viewed prior to the one hour lecture.

SGTA classes (commencing Week 2): There is one two-hour class per week.

The timetable for classes can be found on the University website at: https://timetables.mq.edu.au/2023/. Enrolment can be managed using eStudent at:  https://mq.okta.com/app/mq_estudentnew_1/exkijv9d06f840gdl2p7/sso/saml. 

Technologies used and required

Lecture material will be placed on iLearn. The statistical package SPSS will be used.

Recommended Texts

There is no set textbook for this unit.

Useful reference texts for the Market Research part of the unit are

  • Applied Multivariate Techniques by Subhash Sharma (QA278.S485/1996)
  • Applied Multivate 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

  • Rob J Hyndman and George Athanasopoulos (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3/

Communication

We will communicate with you via your university email or through announcements on iLearn. Queries to the convenor can be posted on the iLearn General Discussion, sent via iLearn Private Message to Unit Contact or emailed to your lecturer from your university email address.

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.

 

 

Unit Schedule

Week Topic
1 Principal Component Analysis (PCA)
2 PCA
3 Factor Analysis (FA)
4 FA
5 Conjoint Analysis (CA)
6 Class Test 1
7 Introduction to Forecasting
8 ARIMA models
9 ARIMA models
10 Dynamic Regression models and intervention analysis
11 Exponential smoothing and Periodicity
12 Class Test 2
13 Revision

 

Policies and Procedures

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.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

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

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

The Writing Centre

The Writing Centre provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via AskMQ, or contact Service Connect.

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.

Changes from Previous Offering

Since it is anticipated that this is the last time this unit will be offered, 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.

Changes since First Published

Date Description
25/09/2023 "Tutor" replaced by "Teaching staff"

Unit information based on version 2023.01R of the Handbook