Descriptive Analytics and Visualisations

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MIS771
Descriptive Analytics and
Visualisation
Assignment One

Background
This is an individual assignment. You need to analyse a given data set, and then interpret and draw
conclusions from your analysis. You then need to convey your conclusions using plain language in a
written report to a person with little or no knowledge of Business Analytics.

Percentage of the final grade 30%
The Due Date and Time 11.59 PM Sunday 18th August 2019

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 correctly 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 15th August 2019, 5:00 PM. Please note that assignment extensions will only
be 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.
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 receive 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.
The assignment uses the dataset file A1.xlsx, which can be downloaded from CloudDeakin. Analysis
of the data requires the use of techniques studied in Module-1.
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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
limits 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 feedback will be released via CloudDeakin, usually within 15 working
days. You are expected to refer and compare your answers to the feedback to understand any areas
of improvement.
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Case Study
You are Natalia Navarska, a data analyst in the Research and Analysis group at Financial Review
Magazine. Your primary role is to evaluate new products and services. You are often required to
report outcomes of your analysis to senior editors at the Magazine who have little or no knowledge
of data analysis.
Of specific interest to Financial Review magazine are the increasing numbers of companies that offer
brokerage services for car insurance and potentially what this means for consumers. An insurance
broker is an independent insurance agent who works with many insurance companies to find the
very best available policies for his or her customers. Most of these brokers are advertising that they
can save vehicle owners hundreds of dollars each year on insurance premiums.
Just recently, your research and analysis group secured a dataset from the Insurance Brokers
Association (IBA), which is a random sample of 400 customers who obtained the services of car
insurance brokers. You have performed an exploratory analysis and have emailed the results (see
pages 6-7) to Edmond Kendrick, one of the senior editors of Financial Review Magazine.
Edmond has replied to your email regarding the Insurance Brokers. His email is reproduced next
page:
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Email from Edmond
To: Natalia Navarska
From: Edmond Kendrick
Subject: Analysis of car insurance brokerage services
Hi Nat,
Thank you for the comprehensive analysis and notes. Now I am more curious about what else could
we learn from analysing the dataset.
1. From what I can gather from your notes, iChoose was able to save their customers more money
than other brokers. Can I now conclude that iChoose, on average, can save more on insurance
premiums than uChoose?
2. Your analysis of 400 customers showed that the proportion of dissatisfied (i.e. either
‘Dissatisfied’ or ‘Very Dissatisfied’) urban customers is smaller than the proportion of dissatisfied
rural customers. Can we argue that this difference would hold across all urban and rural
customers?
3. I did my own analysis of the sample and came to the following conclusions:
a. The average savings on insurance premiums differ between rural and urban customers.
b. On average, customers with ‘Agreed Value’ policy saved more on their insurance
premiums than the customers with ‘Market Value’ policy;
c. The proportion of female customers with a diamond level no claim bonus rating (NCBR) is
less than male customers with a diamond level no claim bonus rating (NCBR);
What would be great is if you can verify my findings and tell me how much the difference is in
each of the three scenarios mentioned above.
4. I would like you to expand the analysis and look at whether:
a. The average savings on insurance premiums significantly differ between Victoria, NSW
and Queensland.
b. The average savings on insurance premiums significantly differ between 4WD, Luxury
and Sports car.
5. Does the proportion of customers who approached their insurance provider before reaching out
to a broker differ between the insurance providers?
6. I asked Raj to design an experiment to see the effects of the valuation method and the vehicle
type on savings on insurance premiums, he sent me a table with some numbers (see AppendixA). Can you complete the analysis?
I look forward to your response.
Regards
Eddie
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Appendix- A: Data for the experiment prepared by Raj

Valuation Method 4WD Family Sport Luxury
Agreed Value 1068 169 1799 966
128 150 680 1144
98 -59 373 893
560 22 143 1144
429 108 442 629
Market Value 104 54 99 1273
72 0 156 247
311 94 1084 357
146 84 357 676
135 -10 131 366
Vehicle Type

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An Extract of the Analysis and Notes Prepared by Nat
• A summary of Savings:

Savings
Mean 229.64
Standard Error 16.03
Median 113
Mode 0
Standard Deviation 320.56
Sample Variance 102759.59
Kurtosis 5.46
Skewness 2.08
Range 2043
Minimum -87
Maximum 1956
Sum 91857
Count 400
Q1 12
Q3 357
IQR 345
LF -505.5
UF 874.5
OUTLIERS YES

mary of Saving by Broker (Broker Performance)
0
20
40
60
80
100
120
140
160
180
Frequency
Saving ($)
HISTOGRAM: SAVING
iChoose uChoose vChoose yChoose
Mean 262.442 230.847 137.381 204.188
Standard Error 25.883 36.672 14.330 31.575
Median 127 94.5 123.5 100
Mode 0 0 294 0
Standard Deviation 356.766 311.169 92.868 309.368
Sample Variance 127281.930 96825.934 8624.437 95708.659
Kurtosis 4.121 4.678 -0.461 6.102
Skewness 1.826 1.934 0.442 2.210
Range 2034 1645 392 1738
Minimum -78 -69 -31 -87
Maximum 1956 1576 361 1651
Sum 49864 16621 5770 19602
Count 190 72 42 96
Q1 0 24 65.5 0
Q3 412.5 388.75 200 338
IQR 412.5 364.75 134.5 338
LF -618.75 -523.125 -136.25 -507
UF 1031.25 935.875 401.75 845
OUTLIERS YES YES NO YES
Saving Outcome Count of Customers
Not benefited from (saving < 0) 72
Neither benefited nor lost (saving =
0)
25
Benefited from (saving > 0) 303

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• Customer Satisfaction

Customer Satisfaction Count of Customers
Very Dissatisfied 35
Dissatisfied 57
Satisfied 174
Very Satisfied 134
Total 400

• Customer Satisfaction by Area

Satisfaction
Area Very Dissatisfied Dissatisfied Satisfied Very Satisfied Total
Rural 10 23 32 30 95
Urban 25 34 142 104 305
Total 35 57 174 134 400

Notes to Edmond
Savings:
From a sample of 400 customers,
• On average, car insurance brokers saved their customers $113 (median).
• The middle 50% of customers saved between $12 and $357; a quarter of the customers saved at
most $12; three-quarter of the customers saved no more than $357.
• The savings ranged from a loss of $87 to a substantial gain of $1956.
• Almost 40% of the customers, saved between $1 and $200 on their current insurance premiums;
car insurance brokers have shown their ability to find an appropriate policy for most of their
customers.
• The bulk of the customers have relatively low (in few cases none at all) annual savings on
premium, with a relatively small number having high savings. 89% of customers saved up to
$600; Only 4% of consumers saved between $1000 and $2000; with only 1%; shows that brokers
have the ability to save consumers a massive amount (more than $1600) on their annual
premiums but the prospect of making such savings is low.
• 24% of consumers paid a higher premium than previously or did not save on their annual
premium.
• 18% of customers made a loss; the brokers are claiming to save most customers hundreds of
dollars, but the discussion about the possibility of customers paying more money for the
insurance is missing.
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SUBMISSION
The assignment consists of two parts: Analysis and Report. You are required to submit both your
written report and analysis.
Guidelines for Data Analysis
Read the case study and questions asked by Edmond carefully. Then spend some time reviewing the
data to get a sense of the context. The analysis required for this assignment involves material covered
in Module 1, with the corresponding tutorials being a useful guide.
The analysis should be submitted in the appropriate worksheets in the Excel file. Each question from
the email should be analysed in a separate tab (e.g. Q1, Q2 … or Q3.1, Q3.2 …). You need to add these.
Before submitting your analysis, make sure it is logically organised, and any incorrect or unnecessary
output has been removed. Marks will be penalised for poor presentation or disorganised/incorrect
results.
For all questions in the email, you can assume that:
• 95 % confidence level is appropriate for confidence intervals and;
• 5.0 % level of significance (i.e. α = 0.05) is appropriate for any hypothesis tests.
You can complete all data analysis using the Excel templates provided in the assignment data file. In
choosing the technique to apply for a given question, keep the following in mind:
• Are we dealing with a numerical variable or categorical variable?
• Are we dealings with one sample, two samples or more than two samples situation?
• Are we dealing with independent samples or paired-samples situation?
• Each question must be answered using the most appropriate technique.
• For all hypothesis questions, please formulate your hypotheses, and state them in both
notation and words clearly.
• Even though question(s) may lead you to inferential technique, consider conducting a
descriptive analysis of the sample data first.
ATTENTION!
• When you have established that there is a difference between two means or proportions, we
expect you to estimate and report the difference.
• When you have established that there is a difference between two or more means or
proportions, we expect you to follow up with an appropriate multiple comparison procedure.
You may need to make certain assumptions about the dataset we are using to answer some questions.
For other questions, there will be technical/statistical assumptions that you need to make; for
example, whether to use an equal or an unequal variance test…etc. You need to consider and
incorporate any violations of assumptions such as unequal sample sizes as limitations of your analysis
in your report.
Note: Give the Excel file the following name A1_YourStudentID.xlsx (use a short file name while you
are doing the analysis.
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Guidelines for your Business Report
Once you have completed your data analysis, you need to summarise the key findings for each
question and write a response to Edmond in a report format. Your business report consists of four
sections: Introduction, Main Body, Conclusion, and Appendices. The report should be around 1,500
words.
Use proper headings (e.g. Q1, Q2 … or Q3.1, Q3.2…) and titles in the main body of the report. Use subheadings where necessary.
Keep the language plain and the explanations brief. That is, avoid the use of any unnecessary
technical statistical jargon. Your reader may not necessarily understand even the simplest statistical
term. Thus your task is to convert your analysis into plain, easily understandable expressions.
General instructions:
• You MUST report both descriptive and inferential analysis results. Otherwise, marks will be
deducted.
• The report is to be written as a stand-alone document (assume Edmond will only read your
written report). Thus, you should not have any direct references in the report to your analysis.
• Your report may include relevant excel outputs including templates, tables, charts, and graphs
but ONLY as Appendices (appendices are not included in the word count).
• Make sure these outputs in the Appendix are visually appealing, have a consistent formatting
style and proper titles (title, axes titles, etc.), and are numbered correctly.
• The introduction begins by highlighting the main purpose(s) of the analysis and concludes by
explaining the structure of the report (i.e., subsequent sections). The conclusion should
highlight the key findings of the analyses and explain the main limitations (if any).
• Marks will be deducted for the use of technical terms, irrelevant material, and poor
presentation/organisation.
When you have completed the report, it is a useful exercise to leave it for a day, return to it and then
re-read. Does it flow easily? Does it make sense? Can someone without prior knowledge follow your
written conclusions? Often, on re-reading, you become aware that you have made some points
clumsily, and you find that you can re-phrase them much more clearly.
Note: Give the report the following name A1_YourStudentID.docx or pdf.