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THE UNIVERSITY OF NEW SOUTH WALES
SCHOOL OF INFORMATION SYSTEMS AND TECHNOLOGY MANAGEMENT
TERM 3 2021
INFS5720: BUSINESS ANALYTICS METHODS
FINAL EXAMINATION
1. Time Allowed: 24 Hours.
2. This is a Take-Home Exam, your responses must be your own original work.
You must attempt this Take-Home Exam by yourself without any help from
others. Thus, you have NOT worked, collaborated or colluded with any other
persons in the formulation of your responses. The work that you are submitting
for your Take-Home Exam is your OWN work.
3. Release date/time (via Moodle): Tuesday, 30 November 9:00am (Australian
Eastern Time Zone)
4. Submission date/time (Via Turnitin): Wednesday, 1 December 9:00am
(Australian Eastern Time Zone)
5. Failure to upload the exam by the submission time will result in a penalty of
15% of the available marks per hour of lateness.
6. This Examination Paper has 4 pages, including the cover page.
7. Total number of Questions: 5 Questions.
8. Answer all 5 Questions.
9. Total marks available: 100 marks. This examination is worth 55% of the total
marks for the course.
10. Questions are not of equal value. Marks available for question sub-parts are
shown on this examination paper.
11. Some questions have word limits as indicated on the question. These word
limits must be adhered to. Text in excess of the specified word limit(s) may not
be considered in the marking process.
12. Candidates must submit a signed Declaration Form together with the Take-
Home Exam answer document. Failure to submit the signed Declaration Form
may result in your Take-Home Exam answer sheet not being marked.
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13. Answers to questions are to be written in the template provided. Please ensure
that you provide the following details on your Take-Home Exam answer sheet:
• Student ID:
14. In accordance with the Declaration Form, this Take-Home Exam paper cannot
be copied, forwarded or shared.
15. Students are reminded of UNSW’s rules regarding Academic Integrity and
Plagiarism. Plagiarism is a serious breach of ethics at UNSW and is not taken
lightly. For details see Examples of plagiarism.
16. This Take-Home Exam is an open book/open web, further information is
available “Here”.
• You are permitted to refer to your course notes, any materials provided by
the course convenor or lecturer, books, journal articles, or tutorial
materials.
• It is sufficient to use in-text citations that include the following information:
the name of the author or authors; the year of publication; the page
number (where the information/idea can be located on a particular page
when directly quoted), For example, (McConville, 2011, p.188).
• You are required to cite your sources and attribute direct quotes
appropriately when using external sources (other than your course
materials).
• When citing Internet sources, please use the following format:
website/page title and date.
• If you provide in-text citations, you MUST provide a Reference List. The
Reference list will NOT BE counted towards your word limit.
17. Students are advised to read the Take-Home Exam paper thoroughly before
commencing.
18. The Lecturer-in-Charge (LiC) / Exam Referee will be available online (via
Moodle) after the Take-Home Exam paper is released for a period of two
hours.

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You need to answer Questions 1, 2, 3, and 4 based on a business case: Retail Credit
Scoring for Auto Finance Ltd. Please access the case via the link provided on Moodle.
You DO NOT need to answer the questions on the case document (if any).
QUESTION 1 25 MARKS
With reference to the above case, answer all of the following questions:
a) Based on the business challenges that Auto Finance Ltd. is facing, list and
explain a segmentation problem and a classification problem respectively.
[max 200 words] (10 marks)
b) Select an analytics model for each problem you have identified in Q1a and justify
your choice. [max 100 words] (5 marks)
c) Outline relevant variables (i.e., target variables; input variables) for each analytics
model you have identified in Q1(b). Justify your choice of these variables.
[max 200 words] (10 marks)


QUESTION 2 15 MARKS
Explain how each of your proposed modelling techniques in Question 1 b) can help
Auto Finance Ltd. generate insights about a potential customer defaulting on a loan.
[max 300 words]



QUESTION 3 10 MARKS
With reference to the above case, describe the pros and cons of using a metric
accuracy vs. recall vs. Area-Under-Curve for your classification modelling. Justify
which metric is the best based on the context of the case. [max 250 words]



QUESTION 4 15 MARKS
There are concerns about the ethical issues involved in scoring a customer’s credits.
Identify three ethical issues that Auto Finance Ltd. could face. Provide your
recommendations to mitigate each ethical issue identified. [max 300 words]


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QUESTION 5 35 MARKS
To answer the following questions, you need to deploy the dataset “Cars” on SAS Viya
and perform the analysis using SAS Viya. The description of variables in the dataset
is shown in the table below.
No Variables Description
1 Make Manufacturing company
2 Model Model name
3 DriveTrain Type of drivetrain (e.g., front wheel drive; rear wheel drive)
4 Origin Regions by continent
5 Cylinders Number of cylinders
6 EngineSize Engine displacement size (in litres)
7 Horsepower Maximum horsepower
8 Invoice Retail price shown on an invoice
9 MSRP Manufacturer's suggested retail price
10 Length Length of a vehicle
11 MPG_Highway How far the car is able to travel for every gallon of fuel it uses on the highway
12 MPG_City How far the car is able to travel for every gallon of fuel it uses around the city
13 Type Type of a car
14 Weight Weight of a car
15 Wheelbase The distance between the front and rear axles of a car

a) Given the choice of linear regression vs. logistic regression vs. decision tree,
select a modelling technique that is suitable for evaluating a car’s travelling
capability around the city and justify your choice.
[max 200 words] (10 marks)
b) Based on the modelling technique you selected in Question 5a), report your
model by specifying variables (i.e., dependent variables; independent variables),
reporting corresponding parameters and fit statistics. Note: specify intercept and
coefficient parameters if you develop a linear or logistic regression model. Specify
decision rules if you develop a decision tree model.
[No word limit] (10 marks)


c) Based on the output of your model in Question 5b), explain the impact of each
independent variable you chose on a car’s travelling capability around the city.
[300 words] (15 marks)
— END OF EXAMINATION PAPER —