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Unit outline_

QBUS3840: Choice Modelling

Semester 2, 2022 [Normal day] - Camperdown/Darlington, Sydney

How do business analysts model firm or consumer behaviour quantitatively How do analysts model brand choice in marketing or travel mode choice in transport These questions are answered by modelling choices with statistical tools designed for qualitative or discrete data such as logistic regressions rather than the standard linear regression models This unit investigates various quantitative modelling techniques relevant for choice modelling through business cases in marketing transport research strategy economics and other relevant business fields This unit also explores models that pool observations on a crosssection of households countries firms etc over several time periods This is known as panel data models which are increasingly relevant in all areas of Business with the growing availability of new sources of data

Unit details and rules

Academic unit Business Analytics
Credit points 6
Prerequisites
? 
QBUS2810 or DATA2002 or ECMT2110
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Pablo Montero Manso, pablo.monteromanso@sydney.edu.au
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final exam
Written exam
40% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
In-semester test (Take-home short release) Type D in-semester exam In-semester exam
Students analyse a dataset of a case study of choice modelling.
20% Week 08
Due date: 20 Sep 2022 at 13:00
1.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Assignment group assignment Assignment
n/a
40% Week 12 up to 30 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
group assignment = group assignment ?
Type D final exam = Type D final exam ?
Type D in-semester exam = Type D in-semester exam ?

Assessment summary

Assignment: Students will have to find a suitable problem for Choice Modelling that involves complex data types. They will write a full report on their analysis of their chosen problem. They will have to demonstrate the use of the more advanced tools given in class.

Mid semester exam: Students analyse a dataset of a case study of choice modelling. They will have to use the tools given in the lectures and tutorials during the first part of the semester.

They will have to answer questions and draw conclusions from their analysis, demonstrating their mastery of the material.

The exam will involve the use of python programming, creating an executable script (notebook) that will be uploaded to Canvas.

Final Exam: Students analyse a dataset of a case study of choice modelling.

 The analysis will involve the use of all the material given in the unit.
 The exam will involve the use of python programming, creating an executable script (notebook) that will be uploaded to Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 

Distinction

75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Credit

65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Pass

50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Multiple weeks Binary Choice Models: Logit and Probit. The fundamental models for modelling choice, when individuals have to choose between two alternatives. How to estimate these models from data, maximum likelihood. How to interpret the models. Lecture and tutorial (7 hr) LO1 LO2 LO4
Multiple Choice Models: Multinomial Expand the basic models the case where the choice is between multiple alternatives. The most important tool in the modeller's toolbox. Lecture and tutorial (3.5 hr) LO1 LO2
Ordered choice and the Nested Logit. Some limitations and paradoxes in the basic choice models and how to solve them. Lecture and tutorial (3.5 hr) LO1 LO2 LO4
Panel data. How to use data involving repeated measures of the choice of individuals over time. Data pooling to improve estimation, modelling variations of choice over time and variations specific to individuals. Lecture and tutorial (3.5 hr) LO1 LO2 LO4
Design of experiments for Choice Modelling. How to design cost-efficient experiments that elucidate the preferences of a given target population. Lecture and tutorial (7 hr) LO3 LO4
Nonlinear Modelling of Choice. Interpretability. Modern Machine Learning / Artificial Intelligence tools for predicting choice. Interpretation of these models for further insights into decision-making processes. Lecture and tutorial (7 hr) LO1 LO2 LO4 LO5
Advanced sources of data. Spatial, Temporal and Text data. Introducing non-conventional sources of data into classical choice modelling and modern machine learning. Lecture and tutorial (3.5 hr) LO1 LO2 LO4 LO5
Practical Real-world choice prediction of choice. In-depth analysis of real cases from industry and academia where choice modelling has been successfully applied. Lecture and tutorial (3.5 hr) LO1 LO2 LO3 LO4 LO5
Concluding lecture. We will summarize on what we have learned, reflect, highlight the points and put everything in perspective. Lecture and tutorial (3.5 hr) LO1 LO2 LO3 LO4 LO5
Week 01 Introduction. Behavioural Models. Utility. An overview of the unit. Using mathematics to express how individuals make decisions. Lecture (1.5 hr) LO1 LO2
Getting started with the software for data analysis. Markdown. python. Notebooks. Tutorial (2 hr) LO4 LO5

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

Material will be provided on Canvas.

Applied Choice Analysis, 2nd Edition. Hensher, Rose and Greene. 2015.

 

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. predict consumer behaviour based on mathematical models derived from experimental data
  • LO2. interpret, explain and criticise the results of model-based analysis of consumer choice.
  • LO3. design experiments and curate existing data to capture consumer behaviour in a cost-efficient manner.
  • LO4. use and extend data analysis software tools for modelling choice.
  • LO5. incorporate modern machine learning techniques to choice analysis and prediction.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

This is the first time this unit has been offered

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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