Students
STAT328 – Market Research and Forecasting
2014 – S2 Evening
General Information
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| Unit convenor and teaching staff |
Unit convenor and teaching staff
Lecturer
Tania Prvan
Contact via 9850 8561
E4A 531
Wednesday 4pm - 6pm
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| Credit points |
Credit points
3
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| Prerequisites |
Prerequisites
39cp including (STAT270(P) or STAT271(P) or BIOL235(P) or PSY222(P) 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
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.
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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:
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
- Know why you would want to smooth the data first. Be able to use a statistical package to smooth the data and then fitting an ARIMA to the smoothed data.
Assessment Tasks
| Name | Weighting | Due |
|---|---|---|
| Assignment 1 | 5% | 29 August |
| Assignment 2 | 5% | 17 October |
| Assignment 3 | 5% | 7 November |
| Test 1 | 10% | 10 September |
| Test 2 | 10% | 29 October |
| HW & Tutorial Participation | 5% | Weekly |
| Final Examination | 60% | TBA |
Assignment 1
Due: 29 August
Weighting: 5%
Online submission through iLearn by 2 pm on the due date. Must be submitted as a pdf. This can be done in Word or using freeware like Cute PDF Writer. There is no "group work" assessment in this unit. All work is to be the student's own.
On successful completion you will be able to:
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
Assignment 2
Due: 17 October
Weighting: 5%
Online submission through iLearn by 2 pm on the due date. Must be submitted as a pdf. This can be done in Word or using freeware like Cute PDF Writer. There is no "group work" assessment in this unit. All work is to be the student's own.
On successful completion you will be able to:
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
Assignment 3
Due: 7 November
Weighting: 5%
Online submission through iLearn by 2 pm on the due date. Must be submitted as a pdf. This can be done in Word or using freeware like Cute PDF Writer. There is no "group work" assessment in this unit. All work is to be the student's own.
On successful completion you will be able to:
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
- Know why you would want to smooth the data first. Be able to use a statistical package to smooth the data and then fitting an ARIMA to the smoothed data.
Test 1
Due: 10 September
Weighting: 10%
This will be held in the second hour of the lecture in Week 6.
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).
Failure to attend the test without relevent documentation to explain the absence will result in zero marks being awarded for the test. The test is compulsory and there will be NO supplementary class tests.
The procedure for submitting a Special Consideration online can be obtained by visiting ask.mq.edu.au.
Test 1 covers lecture material from Weeks 1 - 5 inclusive and will be of 50 minutes duration.
On successful completion you will be able to:
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
Test 2
Due: 29 October
Weighting: 10%
This will be held in the second hour of the lecture in Week 11.
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).
Failure to attend the test without relevent documentation to explain the absence will result in zero marks being awarded for the test. The test is compulsory and there will be NO supplementary class tests.
The procedure for submitting a Special Consideration online can be obtained by visiting ask.mq.edu.au.
Test 2 covers lecture material from Weeks 6 - 10 inclusive and will be of 50 minutes duration.
On successful completion you will be able to:
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
HW & Tutorial Participation
Due: Weekly
Weighting: 5%
Every week tutorial participation will be monitored and most weeks there will be set homework to submit to the lecturer at the start of the following lecture.
On successful completion you will be able to:
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
- Know why you would want to smooth the data first. Be able to use a statistical package to smooth the data and then fitting an ARIMA to the smoothed data.
Final Examination
Due: TBA
Weighting: 60%
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).
The final examination will cover all topics in the unit. Students may take into the final Exam ONE A4 page of notes handwritten (not typed) on BOTH sides. Calculators will be needed but must not be of the text/programmable type.
You are expected to present yourself for examination at the time and place designated in the University Examination Timetable. The 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.
The only exemptions to sitting an Examination at the designated time are because of documented illness or unavoidable disruption. In these circumstances you may wish to consider applying for Disruption to Studies (formerly known as Special Consideration). Information about unavoidable disruption and the disruption to studies process is available at
http://students.mq.edu.au/student_admin/exams/disruption_to_studies/
Students need to apply for Disruption to Studies online at https://ask.mq.edu.au
If a Disruption to Studies is granted as a result of the Disruption to Studies process the Examination will be scheduled after the conclusion of the official examination period.
On successful completion you will be able to:
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
- Know why you would want to smooth the data first. Be able to use a statistical package to smooth the data and then fitting an ARIMA to the smoothed data.
Delivery and Resources
There is one two hour lecture and one two hour practical each week in this unit. Lectures commence in Week 1 and practicals commence in Week 2.
Technologies used and required
Lecture material will be placed on iLearn. The statistical package SPSS will be used in some of the lectures.
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 reference text for Conjoint Analysis. Most treatments in Market Research textbooks are either too simple or too technical.
A useful reference for the Forecasting part of the unit is
- Forecasting: Methods and Applications by Spyros Makridakis, Steven C. Wheelwright and Rob J. Hyndman (HD30.27.M34/1998)
Unit Schedule
| Week | Topic |
| 1 (4 August) | Principal Component Analysis (PCA) |
|
2 (11 August) |
PCA |
| 3 (18 August) | PCA and Factor Analysis (FA) |
| 4 (25 August) | FA |
| 5 (1 September) | Conjoint Analysis (CA) |
| 6 (8 September) |
CA |
| 7 (15 September) |
Introduction to Time Series |
| 8 (7 October) | ARIMA models |
| 9 (13 October) | ARIMA models |
| 10 (20 October) | Dynamic regression models and intervention analysis |
| 11 (27 October) |
Exponential Smoothing Class Test 2 |
| 12 (3 November) | Periodicity |
| 13 (10 November) | Revision |
Learning and Teaching Activities
PCA
FA
CA
Time Series 1
Time Series 2
Time Series 3
Policies and Procedures
Macquarie University policies and procedures are accessible from Policy Central. Students should be aware of the following policies in particular with regard to Learning and Teaching:
Academic Honesty Policy http://mq.edu.au/policy/docs/academic_honesty/policy.html
Assessment Policy http://mq.edu.au/policy/docs/assessment/policy.html
Grading Policy http://mq.edu.au/policy/docs/grading/policy.html
Grade Appeal Policy http://mq.edu.au/policy/docs/gradeappeal/policy.html
Grievance Management Policy http://mq.edu.au/policy/docs/grievance_management/policy.html
Disruption to Studies Policy http://www.mq.edu.au/policy/docs/disruption_studies/policy.html The Disruption to Studies Policy is effective from March 3 2014 and replaces the Special Consideration Policy.
In addition, a number of other policies can be found in the Learning and Teaching Category of 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/support/student_conduct/
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://informatics.mq.edu.au/help/.
When using the University's IT, you must adhere to the Acceptable Use Policy. The policy applies to all who connect to the MQ network including students.
Graduate Capabilities
Capable of Professional and Personal Judgement and Initiative
We want our graduates to have emotional intelligence and sound interpersonal skills and to demonstrate discernment and common sense in their professional and personal judgement. They will exercise initiative as needed. They will be capable of risk assessment, and be able to handle ambiguity and complexity, enabling them to be adaptable in diverse and changing environments.
This graduate capability is supported by:
Learning outcomes
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
Assessment tasks
- Assignment 1
- Assignment 2
- Assignment 3
- HW & Tutorial Participation
- Final Examination
Learning and teaching activities
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
- Know why you would want to smooth the data first. Be able to use a statistical package to smooth the data and then fitting an ARIMA to the smoothed data.
Discipline Specific Knowledge and Skills
Our graduates will take with them the intellectual development, depth and breadth of knowledge, scholarly understanding, and specific subject content in their chosen fields to make them competent and confident in their subject or profession. They will be able to demonstrate, where relevant, professional technical competence and meet professional standards. They will be able to articulate the structure of knowledge of their discipline, be able to adapt discipline-specific knowledge to novel situations, and be able to contribute from their discipline to inter-disciplinary solutions to problems.
This graduate capability is supported by:
Learning outcomes
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
- Know why you would want to smooth the data first. Be able to use a statistical package to smooth the data and then fitting an ARIMA to the smoothed data.
Assessment tasks
- Assignment 1
- Assignment 2
- Assignment 3
- Test 1
- Test 2
- HW & Tutorial Participation
- Final Examination
Learning and teaching activities
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
- Know why you would want to smooth the data first. Be able to use a statistical package to smooth the data and then fitting an ARIMA to the smoothed data.
Critical, Analytical and Integrative Thinking
We want our graduates to be capable of reasoning, questioning and analysing, and to integrate and synthesise learning and knowledge from a range of sources and environments; to be able to critique constraints, assumptions and limitations; to be able to think independently and systemically in relation to scholarly activity, in the workplace, and in the world. We want them to have a level of scientific and information technology literacy.
This graduate capability is supported by:
Learning outcomes
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
- Know why you would want to smooth the data first. Be able to use a statistical package to smooth the data and then fitting an ARIMA to the smoothed data.
Assessment tasks
- Assignment 1
- Assignment 2
- Assignment 3
- Test 1
- Test 2
- HW & Tutorial Participation
- Final Examination
Learning and teaching activities
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
- Know why you would want to smooth the data first. Be able to use a statistical package to smooth the data and then fitting an ARIMA to the smoothed data.
Problem Solving and Research Capability
Our graduates should be capable of researching; of analysing, and interpreting and assessing data and information in various forms; of drawing connections across fields of knowledge; and they should be able to relate their knowledge to complex situations at work or in the world, in order to diagnose and solve problems. We want them to have the confidence to take the initiative in doing so, within an awareness of their own limitations.
This graduate capability is supported by:
Learning outcomes
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
- Know why you would want to smooth the data first. Be able to use a statistical package to smooth the data and then fitting an ARIMA to the smoothed data.
Assessment tasks
- Assignment 1
- Assignment 2
- Assignment 3
- Test 1
- Test 2
- HW & Tutorial Participation
- Final Examination
Learning and teaching activities
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
- Know why you would want to smooth the data first. Be able to use a statistical package to smooth the data and then fitting an ARIMA to the smoothed data.
Creative and Innovative
Our graduates will also be capable of creative thinking and of creating knowledge. They will be imaginative and open to experience and capable of innovation at work and in the community. We want them to be engaged in applying their critical, creative thinking.
This graduate capability is supported by:
Learning outcomes
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
- Know why you would want to smooth the data first. Be able to use a statistical package to smooth the data and then fitting an ARIMA to the smoothed data.
Assessment tasks
- Assignment 1
- Assignment 2
- Assignment 3
- HW & Tutorial Participation
Learning and teaching activities
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
- Know why you would want to smooth the data first. Be able to use a statistical package to smooth the data and then fitting an ARIMA to the smoothed data.
Effective Communication
We want to develop in our students the ability to communicate and convey their views in forms effective with different audiences. We want our graduates to take with them the capability to read, listen, question, gather and evaluate information resources in a variety of formats, assess, write clearly, speak effectively, and to use visual communication and communication technologies as appropriate.
This graduate capability is supported by:
Learning outcomes
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
- Know why you would want to smooth the data first. Be able to use a statistical package to smooth the data and then fitting an ARIMA to the smoothed data.
Assessment tasks
- Assignment 1
- Assignment 2
- Assignment 3
- Test 1
- Test 2
- HW & Tutorial Participation
- Final Examination
Learning and teaching activities
- Know when it is appropriate to carry out a principal component analysis (PCA), whether to use a correlation matrix or covariance matrix for the PCA, be able to perform a PCA using a computer package, be able to interpret the output from a PCA and determine how many principal components to use.
- Know when it is appropriate to carry out a Factor Analysis (FA), be able to perform the appropriate FA using a statistics package, know how to interpret the factors, determine how many factors to retain, and be aware of the difference between a FA and PCA.
- Know how to conduct a Conjoint Analysis (CA) and how to generate an orthogonal plan.
- Know what a time series model is and be familiar with the two major types of forecasting models. Be able to fit AR, MA and ARIMA models using a statistics package and be able to interpret the output. Be able to to decide which is the appropriate model to fit to the time series data.
- Know how to combine a regression and an ARIMA in the same model. Be able to fit such a model using a statistical package and be able to interpret the output.
- Know why you would want to smooth the data first. Be able to use a statistical package to smooth the data and then fitting an ARIMA to the smoothed data.