MIS771
MIS771 - Descriptive Analytics and Data Visualisation
Trimester 2, 2018
Page 1 of 10
MIS771
Descriptive Analytics and
Visualisation
Assignment Two
Background
This assessment task is an individual assignment, which requires you to analyse a given data set, interpret
and draw conclusions from your analysis, and then convey your findings in a written technical report to
an expert in Business Analytics.
Percentage of final grade 35%
The Due Date and Time 11.59 PM Sunday 16th September 2018 AEST
Submission instructions
The assignment must be submitted by the due date electronically in CloudDeakin. When submitting
electronically, you must check that you have submitted the work co
ectly by following the instructions
provided in CloudDeakin. Please note that we will NOT accept any paper or email copies, or part of the
assignment submitted after the deadline.
No extensions will be considered unless a written request is submitted and negotiated with the Unit
Chair before Thursday 13th September 2018, 5:00 PM. Please note that assignment extensions will only
e considered if you attach your draft assignment with your request for an extension.
You must keep a backup copy of every assignment you submit (that is, the work you have done to date)
until the assignment has been marked. In the unlikely event that an assignment is misplaced, you will need
to submit your backup copy. Work you submit will be checked by electronic or other means to detect
collusion and/or plagiarism.
When you submit an assignment through your CloudDeakin unit site, you will receive an email to your
Deakin email address confirming that the assignment has been submitted. You should check that you can
see your assignment in the Submissions view of the Assignment Dropbox folder after upload, and check
for, and keep, the email receipt for the submission.
MIS771 - Descriptive Analytics and Data Visualisation
Trimester 2, 2018
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Penalties for late submission: The following marking penalties will apply if you
submit an assessment task after the due date without an approved extension: 5%
will be deducted from available marks for each day up to five days, and work that is
submitted more than five days after the due date will not be marked. You will
eceive 0% for the task. 'Day' means calendar days or part thereof. The Unit Chair
may refuse to accept a late submission where it is unreasonable or impracticable to
assess the task after the due date.
For more information about academic misconduct, special consideration, extensions,
and assessment feedback, please refer to the document Your rights and
esponsibilities as a student in this Unit in the first folder next to the Unit Guide of
the Resources area in the CloudDeakin unit site.
The assignment uses the file stores.xlsx, which can be downloaded from CloudDeakin. Analysis of the data
equires the use of techniques studied in Module 2.
Assurance of Learning
This assignment assesses the following Graduate Learning Outcomes and related Unit Learning
Outcomes:
Graduate Learning Outcome (GLO) Unit Learning Outcome (ULO)
GLO1: Discipline-specific knowledge and
capabilities - appropriate to the level of
study related to a discipline or profession.
GLO3: Digital Literacy - Using technologies to find,
use and disseminate information
GLO5: Problem Solving - creating solutions to
authentic (real-world and ill-defined)
problems.
ULO 1: Apply quantitative reasoning skills to solve
complex problems.
ULO 2: Use contemporary data analysis and
visualisation tools and recognise the
limitation of such tools.
Feedback before submission
You can seek assistance from the teaching staff to ascertain whether the assignment conforms to
submission guidelines.
Feedback after submission
An overall mark together with suggested solutions will be released via CloudDeakin, usually within 15
working days. You are expected to refer and compare your answers to the suggested solutions to
understand any areas of improvement.
MIS771 - Descriptive Analytics and Data Visualisation
Trimester 2, 2018
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Case Study (Background to Furphy)
Furphy is one of Australia's leading supermarket chains. There are 700 stores in the chain. Originating
from a family based chain of general stores, Furphy now has stores all over Australia, with the first one
eing established 27 years ago. Regarding operation, individual store management has wide-ranging
powers about the day-to-day operations of their stores. However, Furphy’s strategic planning and
direction take place in the company Head Office in Melbourne.
In 2016, Furphy Head Office asked all store managers to add an online channel to their stores to enable
customers, in their subu
s, to make their purchase online.
Despite their successful operations and solid financial turnovers in the last two years, Furphy is forecasting
a shift in the business climate within the next five years. This is a result of ever-increasing competition in
the grocery supermarket sector. Now more than ever, Furphy management feels the need to ensure a
good understanding of their business performance. The Furphy Head Office is slightly confused about the
lack of enthusiasm of store managers to open their online sales channel given that Furphy Head office has
invested heavily on a digital platform and distribution partnership with a transport company. Also, they
are planning to put in place a formal procedure to forecast their Sales.
Subsequently, Furphy has approached BEAUTIFUL-DATA (a market research company) and asked them to
conduct a study to understand the characteristics of Furphy’s stores and their business performance.
The Data
For this study, BEAUTIFUL-DATA has collected two sets of Data:
1. The data related to stores were extracted from the Furphy’s datamarts. It is a random sample of 150
stores in the Furphy chain. A complete listing of variables, their definitions, and an explanation of their
coding are provided in Working Sheet “Stores-Variable Description.
2. Time-series data is available on Working Sheet “Quarterly Sales”.
Your Role as a BEAUTIFUL-DATA Data Analyst Intern
You are a Master of Business Analytics student doing an internship at BEAUTIFUL-DATA. The research
team manager (Todd Nash, with a PhD in Data Science and a Master Degree in Digital Marketing) has
asked you to lead the data analysis process for the Furphy project and directly report the results to him.
You and Todd just finished a meeting wherein he
iefed you on the vital purpose of the project.
Todd explained that a model should be built to estimate Furphy’s Sales. Therefore, the first goal is to
identify key factors that influence Sales. The second goal is to understand the relationship between
number of competitors and Sales. He is also interested in gaining insights into factors that influence
Furphy stores to open online sales channels. The final goal is to construct a forecasting model, which
forecast Furphy’s Sales in the upcoming four quarters. From these insights, Todd and consequently Furphy
will be in an excellent position to develop plans for the next financial year.
MIS771 - Descriptive Analytics and Data Visualisation
Trimester 2, 2018
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Todd also allocated relevant research tasks and explained his expectations from your analysis in the
meeting. Minutes of this meeting are available on the next page.
Now, your job is to review and complete the allocated tasks as per this document.
MIS771 - Descriptive Analytics and Data Visualisation
Trimester 2, 2018
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BEAUTIFUL-DATA,
727 Collins St, Docklands
VIC 3008
Phone: XXXXXXXXXX)
XXXXXXXXXX
Reference PH-102 Furphy Project
Revised 06th August, 2018
Level Expert Analysis
Meeting Chair Todd Nash
Date 06th August 2018 Time 11:00 AM Location BEAUTIFUL-DATA F3.101
Topic Furphy Project – Analytics Details
Meeting
Purpose: Specifying and Allocating Data Analytics Tasks
Discussion
items:
• Variable(s) description.
• Modelling Sales.
• Modelling the likelihood of opening an online channel.
• Forecasting Sales in the upcoming four quarters.
• Producing a technical report.
Detailed
Action Items
Who:
Graduate
Intern
What:
1. Provide an overall summary of the following two variables:
1.1. Sales
1.2. Online Sales Channel
2. Identify potential factors that may influence Sales:
2.1. An appropriate statistical technique could be used here to identify a list of
possible factors.
2.2. Build a model (through a model building process) to estimate Sales.
2.3. Todd has done a regression analysis to predict sales using number of
competitors and stores open on Sundays. He believes that the relationship
etween number of competitors and sales should be weaker for those stores
that are open on Sundays. Your task here is to test Todd’s assumption by
modelling the interaction between the predictors mentioned above and the
target variable. Comment whether there is sufficient evidence that the
interaction term makes a significant contribution to the model.
3. Finalise the model to predict the likelihood of opening an online sales channel:
3.1. Todd has already done an initial analysis for this task. Based on his analysis,
Todd has na
owed down the key predictors of the likelihood of opening an
online sales channel to “Manager’s Age, Experience and Gender”. Your task
now is to continue his work and develop a predictive model to ascertain the
MIS771 - Descriptive Analytics and Data Visualisation
Trimester 2, 2018
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“likelihood of opening an online sales channel”. Todd is specifically interested
in understanding the probability of stores which meet the following criteria
to open an online sales channel:
Those stores with managers,
a) in their mid-thirties;
) with varying levels of Management Experience (i.e. 2-16 years?);
c) and across both, male and female store managers.
3.2. Todd believes that the age, experience and the gender of the store manager
may influence the decision to open an online sales channel. Therefore, it is
essential for Furphy to know whether effort and money should be put into
ecruiting tech-savvy young managers. Accordingly, your job is to visualise
the predicted probability of opening online sales channels with the