MIS171 – Business Analytics – Trimester 1 2023
Assessment Task 3 – Individual

Monday, 29 May 2023, by 8:00pm (Melbourne time)
You will submit to unit site:
– one Excel file, with your analysis, and

– one Word file, with your written report
The assignment requires that you analyse a data set, interpret, and draw conclusions from your
analysis, and then convey your conclusions in a written report. The assignment must be completed
individually and must be submitted electronically in CloudDeakin by the due date. When submitting
you must check that you have submitted the work correctly by following the
instructions provided in CloudDeakin. Hard copies or assignments submitted via email will NOT be
The assignment uses the file
DreamCruise dataset A3.xlsx which can be downloaded from
CloudDeakin. The assignment focuses on materials presented up to and including Week 11. The Excel
file which has been provided has different worksheets explaining and containing the DreamCruise
dataset. For confidentiality reasons actual data has not been used in the assessment task. Following
is an introduction to this scenario and detailed guidelines.
Context/Scenario: DreamCruise Passenger Analysis
DreamCruise1 is an established holiday company that specializes in providing leisure cruises from
various ports in Australia to islands in the South Pacific. Before the Covid-19 pandemic, DreamCruise
was a thriving business with a loyal customer base. However, like many other companies in the
tourism industry, DreamCruise has been severely impacted by the pandemic and has had to suspend
its operations.
DreamCruise is a fictitious company and not meant to bear any resemblance to any existing company. All data and any individuals mentioned
are fictitious and have been produced by the Unit team.

As the world looks forward to a post-Covid era, DreamCruise is now exploring ways to relaunch itself
and rebuild its business.
Assume that you are a business analyst recruited by DreamCruise. You have received an email from
Maria Rodriguez, DreamCruise’s Director of Analytics. Your response will be used as part of a report
to the DreamCruise Board of Directors. Maria’s email together with guidelines (shown
in blue) are
presented below:
Email from Maria Rodriguez
To: You
From: Maria Rodriguez, Director of Analytics, DreamCruise
Subject: Analysis of the DreamCruise’s booking passengers dataset
Hi …,
We are very happy with the strong interest and business generated from the DreamCruise’s booking
passengers dataset. The Board wants a detailed understanding of some of the key aspects of the
bookings. I have attached an Excel file with key data and included some guidelines (shown in
blue) to
direct your work.
Please provide answers to the following questions. Return the Excel file to me. As I have training in
business analytics, I am comfortable with technical language. The Board wants a report from you
which explains the outcome of your analysis. As they do not have the benefit of training in business
analytics your report must present the results of your analysis in plain, straight-forward language. I
have provided a template for you to use.
1. Hypothesis Testing (consider α = 5%)
The Board is concerned about the average passenger satisfaction. It has been suggested at a
recent Board meeting that the average Passenger Satisfaction for every Booking Type, is now
less than 70. Does the data confirm this hypothesis?
To answer this question, you will need to conduct an appropriate hypothesis test for Passenger
Satisfaction for each Booking Type.
2. Multiple Linear Regression Modelling (consider α = 5%)
Passenger Satisfaction is an important measure for DreamCruise, as it represents a major
element of the company’s marketing strategy. Build a multiple regression model to predict
Passenger Satisfaction. Your model should provide insights into which factors have a significant
influence on passenger satisfaction, as well as the ability to predict Passenger Satisfaction for
various scenarios.

For this analysis, you will need to build a multiple regression model using Passenger Satisfaction
as the dependent variable. All other variables in the DreamCruise dataset should be included in
the model, except ID, Age Band, Satisfaction Band, and Spending Band (i.e., exclude ID, Age Band,
Satisfaction Band, and Spending Band from your regression model).
Follow the model building process introduced in the lecture and seminars.
Carefully consider the following:
(a) Transform categorical variables into suitable dummy variables
(i.e., Gender, Cabin, and Booking Type).
Copy the DreamCruise Dataset (excluding ID, Age Band, Satisfaction Band, and Spending
Band from your regression model) to the “Correlation” sheet in the Excel file that has been
provided (no earlier than Column W – be careful not to overwrite the Conclusion,
Correlation Table and Scatter Diagram frames).
i. When transforming
Gender into dummy variables, consider Female as the baseline
; meaning the created dummy variables for Gender should only include Make
(Yes and No) and Other/Prefer not to disclose (Yes and No)
ii. When transforming
Cabin into dummy variables, consider Suite as the baseline
; meaning the created dummy variables for Cabin should only include Luxury
(Yes and No), Porthole (Yes and No), and Internal (Yes and No).
iii. When transforming
Booking Type into dummy variables, consider Solo as the baseline
; meaning the created dummy variables for Booking Type should only include
Double (Yes and No), Group (Yes and No), and Family (Yes and No).
Complete the Dummy Variables Summary table which is in the Conclusion section of the
Correlation worksheet. The table summarises the results of your transformation of
categorical variables into dummy variables.
(b) Using the DreamCruise dataset (which now includes transformed dummy variables) as your
reference, complete the following steps:
Correlation – in the section marked “Correlation Table” (below the Conclusion section
on the “Correlation” worksheet) generate a correlation table. Use the “Correlation”
option in Excel’s Data Analysis ToolPak.
ii. On the correlation table, identify and clearly indicate the Independent Variables which
are (virtually) uncorrelated with the Dependent Variable (i.e., all IVs which have a
correlation coefficient with the DV of between -0.05 and 0.05). These IVs are to be
removed from the model prior to running the first iteration of the regression model.
iii. Complete the Uncorrelated Independent Variables summary table which is in the
Conclusion section of the Correlation worksheet. This table summarises which
Independent Variables are to be eliminated from the regression model due to being
(virtually) uncorrelated with Passenger Satisfaction (DV).

iv. Multi-collinearity – review the correlation table for instances of multi-collinearity
between Independent Variables (IV). In cases of multicollinearity, identify and clearly
indicate the IVs with the weakest correlation with the Dependent Variable.
These IVs are to be removed from the model prior to running the first iteration of the
regression model.
v. Complete the Multi-Collinearity summary table which is in the Conclusion section of
the Correlation worksheet. This table summarises which Independent Variables are to
be eliminated from the regression model due to multi-collinearity.
Scatter diagrams – in the section marked “Scatter Diagrams” (below the Correlation
Table section on the “Correlation” worksheet) generate three scatter diagrams, for:
Passenger Satisfaction (Dependent Variable, DV) and the numerical (not
categorical) Independent Variable (IV) which has the highest correlation with the
DV. Include a calculation of the correlation coefficient. Format the diagram, and
include a linear trendline,
Passenger Satisfaction (DV) and the numerical (not categorical) Independent
Variable (IV) which has the lowest (i.e., most negative) correlation with the DV.
Include a calculation of the correlation coefficient. Format the diagram, and include
a linear trendline, and
Passenger Satisfaction (DV) the Independent Variable (IV) that is closest to being
uncorrelated with the DV (i.e., correlation coefficient closest to zero). Include a
calculation of the correlation coefficient. Format the diagram and include a linear
(c) Using the DreamCruise dataset as your reference complete the following steps, on the
“Regression Model” spreadsheet in the Excel file that has been provided (the data set
includes the dummy variables you have created and excludes the Independent Variables
which have been eliminated due to multi-collinearity or being uncorrelated with the
Dependent Variable):
i. Using the “Regression” option in Excel’s Data Analysis ToolPak build a multiple
regression model.
Assess the model for overall significance (F test with alpha set at 0.05, i.e.,
Confidence Level = 95%).
ii. If your first iteration of the overall model is found to be significant, in a stepwise
fashion, sequentially (one at a time) remove the Independent Variables that are least
likely to be contributing to any significant change in the Dependent Variable.
You will need to conduct t-tests with alpha set at 0.05 to determine the significance
of the various IVs you exclude and include in your model.

(d) Once you have created a regression model where all the remaining Independent Variables
are contributing significantly to a change in Passenger Satisfaction, copy the Summary
Output of your final multiple regression model and paste it into the Output section of the
“Regression Model” spreadsheet in the Excel file that has been provided,
i. In the
Conclusion section of the “Model” spreadsheet,
Write the (final) multiple regression equation.
Use the format:
Ŷ = ƅ0 + ƅ1X1 + ƅ2X2…
Explain (interpret) the (final) multiple regression equation/model.
(e) Using the final multiple regression equation (from step (d)(i)),
i. In the
Predictions section of the “Regression Model” spreadsheet in the Excel file that
has been provided, for the scenario outlined below:
Calculate a Point Estimate for Passenger Satisfaction (DV),
Calculate a Prediction Interval for Passenger Satisfaction (DV),
Calculate a Confidence Interval for Passenger Satisfaction (DV),
ii. In the
Conclusion section of the “Regression Model” spreadsheet in the Excel file that
has been provided, for the scenario outlined below:
Interpret the Point Estimate
Interpret the Prediction Interval
Interpret the Confidence Interval

Independent Variables Scenario
Age 52
Gender Female
Children 3
Passengers 5
Cabin Luxury
Booking Type: Family
Cabin Cost $1950
Side Trips $270
Insurance $880
Food and Drink $450
Entertainment $20
Merchandise $270
Total Spending $3840

I look forward to receiving details of your analysis and your report.
Data description
The provided data file includes multiple sheets, labelled “Data Description”, “DreamCruise Data Set”
and a worksheet for your dashboard. The “Data Description” sheet describes all the variables used in
the “DreamCruise Data Set” and is copied below for your convenience.

Variable Description
Passenger ID ID of the passenger making the booking (“Booking Passenger”)
Age Age of the Booking Passenger
Age Band Booking passengers have been allocated to one of five age bands: Young (<30
years), Core (30 – 45 years), Prime (46 – 59 years), Mature (60 – 69 years), Senior
(>70 years)
Gender Gender of Booking Passenger: Male, Female, or Other/Prefer Not to Disclose
Children Number of children included in the booking (included in Passengers)
Passengers Number of passengers included in the booking (including children)
Cabin Suite, Luxury, Porthole, Internal
Booking Type: Solo, Double (couple or two friends travelling together), Group, Family
Cabin Cost Cost per passenger on Cabin (included in Total Spending)
Side Trips Spending per passenger on side trips
Insurance Spending per passenger on travel insurance
Food and Drink Spending per passenger on food and drinks
Entertainment Spending per passenger on entertainment
Merchandise Spending per passenger on merchandise
Total Spending Total Spending per passenger (sum of all cost/spending categories)
Spending Band Budget (<$2,500pp), Medium ($2,501 – $3,499), High ($3,500 – $4,499pp),
Premium (>=$4,500pp).
Satisfaction Passenger rating of satisfaction with their experience:
0 – 100 (lowest to highest)


Satisfaction Band Unhappy (<50), Unimpressed (50 – 59), Acceptable (60 – 69), Happy (70 – 79),
Delighted (>80)

Assignment instructions
The assignment consists of two parts.
Part 1: Data Analysis
Your data analysis must be performed on the Assignment 3 Excel file. The file includes tabs for:
Data Description
DreamCruise Survey Data Set
Analysis for Hypothesis Testing
Analysis for Correlation
Analysis for Regression Model Building
When conducting the analysis, you need to apply techniques learnt in the lectures and seminars. The
analysis section you submit should be limited to the Hypothesis Testing, Correlation, and Regression
worksheets of the Excel file. These are the only worksheets which will be marked. Your analysis
should be clearly labelled and grouped around each question. Poorly presented, unorganised analysis
or excessive output will be penalised.
In the
Conclusion section of each worksheet there is space allocated for you to write a succinct
response to the questions posed in Maria’s email (above). When drafting your Conclusion, make sure
that you directly answer the questions asked. Cite (state) the important features of the analysis in
your Output section. Responses in the Conclusion section will be marked.
Use the
Output section for your analysis to complete the analysis as directed in Maria’s email and
supports your response to his questions (which you will write in the Conclusion section). Analysis in
the Output section will be marked. Make sure your analysis and process complete, clear, and easy to
follow. You may need to add (or widen/narrow) rows or columns to present your analysis clearly and
completely. Poorly presented, disorganised analysis or excessive output will be penalised. It is useful
to produce both numerical and graphical analysis. Sometimes something is revealed in one that is
not obvious in the other.

Use the Workings section for calculations and workings that support your analysis. The Workings
section will not be marked.
Part 2: Report
Having analysed the data, including answers (in technical terms) to the Data Analysis questions from
Part 1 you are required to provide a formal report which can be placed before the DreamCruise Board
of Directors. Assume that none of the directors on the Board have any training in statistics; they will
only be familiar with broad generally understood terms (e.g., average, correlation, proportion, and
probability). They will need you to explain more technical terms, such as quartile, mode, standard
deviation, coefficient of variation, correlation coefficient, and confidence interval, etc.
In section 1 of the report a short interpretation of your findings to each question. In section 2 of the
Make TWO (2) recommendations that the DreamCruise Board could consider to maximise
Passenger Satisfaction.
Your recommendations can be based on analysis in this assignment, analysis
from previous assignments and any other analysis that you consider is relevant and adds impact to
your recommendations.
Thoughts to consider in framing your recommendations include:
Specific actions DreamCruise could take to maximise Passenger Satisfaction based on the
outcomes of your regression model.
Specific actions DreamCruise could take to maximise Passenger Satisfaction based on the
outcomes of your analysis from Assignment 1 and Assignment 2.
Specific actions DreamCruise could take to maximise Passenger Satisfaction based on the
outcomes of any additional analysis you perform.
Recommending targeting a group that DreamCruise could pursue that maximises Passenger
The impact of maximising Passenger Satisfaction on the other important outcome of Total
Considering the impact on Passenger Satisfaction of the variables not specifically included in
your regression model.

Recommending targeting a group that DreamCruise could pursue that provides an optimal
balance in terms of all DreamCruise’s corporate KPIs, including Total Spending and Passenger
Make sure that all your recommendations are directly informed by your data analysis. Do not include
any commentary that is not supported by your data analysis.
Highest marks will be awarded to students who draft distinct (i.e., different) recommendations, and
whose recommendations take into account a broad range of (data-supported) considerations.
When exploring data, we often produce more results than we eventually use in the final report, but
by investigating the data from different angles, we can develop a much deeper understanding of the
data. This will be valuable when drafting your written report.
It is useful to produce both numerical and graphical statistical summaries. Sometimes something is
revealed in one that is not obvious in the other.
You are allowed approximately 1,000 words (950 to 1,050 words) for your report. Remember you
should use font size 11 and leave margins of 2.54 cm.
template is provided for your convenience. Carefully consider the following points:
Your report is to be written as a stand-alone document. Assume that your Excel file is for
Maria’s use only and that Maria will only pass your written report directly to the Board.
Keep the English simple and the explanations clear. Avoid the use of technical statistical
jargon. Your task is to convert your analysis into plain, simple, easy to understand language.
Follow the format of the template when writing your report. Delete the report template
instructions (in purple) when drafting your report.
Do not include any charts, graphs, or tables into your Report.
Include a succinct introduction at the start of your report, and a conclusion that clearly
summarises performance against KPIs.
Marks will be deducted for the inclusion of irrelevant material, poor presentation, poor
organisation, poor formatting, and reports that exceed the word limit.
When you have completed drafting your report, it is a useful exercise to leave it for a day, and then
return to it and re-read it as if you knew nothing about the analysis. Does it flow easily? Does it make
sense? Can someone without prior knowledge follow your written conclusions? Often when rereading, you become aware that you can edit the report to make it more direct and clearer.

Learning Outcomes
This task allows you to demonstrate your achievement towards the Unit Learning Outcomes (ULOs)
which have been aligned to the
Deakin Graduate Learning Outcomes (GLOs). Deakin GLOs describe
the knowledge and capabilities graduates acquire and can demonstrate on completion of their
course. This assessment task is an important tool in determining your achievement of the ULOs. If
you do not demonstrate achievement of the ULOs you will not be successful in this unit. You are
advised to familiarise yourself with these ULOs and GLOs as they will inform you on what you are
expected to demonstrate for successful completion of this unit.
The learning outcomes that are aligned to this assessment task are:

Unit Learning Outcomes (ULO) Graduate Learning Outcomes (GLO)
ULO1: Apply quantitative reasoning skills to
analyse business problems.
GLO1: Discipline-specific knowledge and
ULO2: Create data-driven/fact-based solutions to
complex business scenarios.
GLO5: Problem solving
ULO3: Analyse business performance by
implementing contemporary data analysis tools.
GLO3: Digital literacy
ULO4: Interpret findings and effectively
communicate solutions to business problems
GLO2: Communication

You must submit your assignment in the Assignment Dropbox in the unit CloudDeakin site on or
before the due date.
Your submission will comprise of two files:
1. A Microsoft Excel workbook file containing your Analysis (Part 1), on the relevant tabs, and
2. A Microsoft Word document containing your report (Part 2) to Maria.
When uploading your assignment, your submission files should be named:

Word file:
Excel file:
MIS171_T2_YOURStudentID.doc (or .docx), and
MIS171_T2_YOURStudentID.xls (or .xlsx).

Submitting a hard copy of this assignment is not required. You must keep a backup copy of every
assignment you submit until the marked assignment has been returned to you. In the unlikely event
that one of your assignments is misplaced you will need to submit your backup copy.
Any work you submit may be checked by electronic or other means for the purposes of detecting
collusion and/or plagiarism and for authenticating work.
When you submit an assignment through your CloudDeakin unit site, you will receive an email to
your Deakin email address confirming that it 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.
Marking and feedback
The marking rubric indicates the assessment criteria for this task. It is available in the CloudDeakin
unit site in the Assessment folder, under Assessment Resources. Criteria act as a boundary around
the task and help specify what assessors are looking for in your submission. The criteria are drawn
from the ULOs and align with the GLOs. You should familiarise yourself with the assessment criteria
before completing and submitting this task.
Students who submit their work by the due date will receive their marks and feedback on
CloudDeakin 15 working days after the submission date.
Extensions can only be granted for exceptional and/or unavoidable circumstances outside of your
Requests for extensions must be made by 12 noon on the submission date using the online
Extension Request form under the Assessment tab on the unit CloudDeakin site. All requests for
extensions should be supported by appropriate evidence (e.g., a medical certificate in the case of ill

Applications for extensions after 12 noon on the submission date require University level special
and these applications must be must be submitted via StudentConnect in your
DeakinSync site.
Late submission penalties
If you submit an assessment task after the due date without an approved extension or special
consideration, 5% will be deducted from the available marks for each day after the due date up to
seven days*. Work submitted more than seven days after the due date will not be marked and will
receive 0% for the task. The Unit Chair may refuse to accept a late submission where it is
unreasonable or impracticable to assess the task after the due date. *’Day’ means calendar day for
electronic submissions and working day for paper submissions.
An example of how the calculation of the late penalty based on an assignment being due on a Monday
at 8:00pm is as follows:
1 day late: submitted after Monday 11:59pm and before Tuesday 11:59pm– 5% penalty.
2 days late: submitted after Tuesday 11:59pm and before Wednesday 11:59pm – 10% penalty.
3 days late: submitted after Wednesday 11:59pm and before Thursday 11:59pm – 15% penalty.
4 days late: submitted after Thursday 11:59pm and before Friday 11:59pm – 20% penalty.
5 days late: submitted after Friday 11:59pm and before Saturday 11:59pm – 25% penalty.
6 days late: submitted after Saturday 11:59pm and before Sunday 11:59pm – 30% penalty.
7 days late: submitted after Sunday 11:59pm and before Monday 11:59pm – 35% penalty.
The Dropbox closes the Monday after 11:59pm AEST/AEDT time.
The Division of Student Life provides a range of Study Support resources and services, available
throughout the academic year, including Writing Mentor and Maths Mentor online drop ins and the
SmartThinking 24 hour writing feedback service at
this link. If you would prefer some more in depth
and tailored support, make an appointment online with a Language and Learning Adviser.
Referencing and Academic Integrity
Deakin takes academic integrity very seriously. It is important that you (and if a group task, your
group) complete your own work in every assessment task Any material used in this assignment that
is not your original work must be acknowledged as such and appropriately referenced. You can find
information about referencing (and avoiding breaching academic integrity) and other study support
resources at the following website:
Your rights and responsibilities as a student
As a student you have both rights and responsibilities. Please refer to the document Your rights and
responsibilities as a student
in the Unit Guide & Information section in the Content area in the
CloudDeakin unit site.