MIS770 Foundation Skills in Data Analysis
DEPARTMENT OF INFORMATION SYSTEMS AND BUSINESS ANALYTICS
DEAKIN BUSINESS SCHOOL
FACULTY OF BUSINESS AND LAW, DEAKIN UNIVERSITY
Assignment Two
Analysis of Electric Vehicle Data
1.0 Particulars
• Due: Week 9, Thursday 19th January 2023, 8:00 pm (AEST).
• Marks: 30%.
• Words: 2,000 words or approximate equivalent.
Note: Part of your submission involves visualisations (in Excel) which ultimately account for a
proportion of this word count. Accordingly, your report (Word document) should be
approximately 3 pages in length.
• Submission: Two files (Excel, Word) electronically via CloudDeakin.
Email submissions will not be accepted.
Note: Do not convert your Word document to pdf format.
• Notes: This assignment is to be completed individually.
Please ensure you are familiar with the Extension Request and the Late Penalties rules
governing assignments in the Faculty of Business and Law (see details below).
1.1 Assurance of Learning
This assignment assesses the following Graduate Learning Outcomes and related Unit Learning Outcomes:
Graduate Learning Outcome (GLO) Unit Learning Outcome (ULO)
GLO4: Critical thinking: evaluating information using
critical and analytical thinking and judgment
ULO2: Manipulate and summarise data that accurately
epresents real world problems
ULO3: Interpret and appraise statistical output to assist
in real-world decision making
2.0 Overview
The purpose of this assignment is to investigate a dataset that has been produced based on available information on
a sample of electric vehicles (EVs). You now need to inte
ogate this dataset in order to answer questions posed by
the Australian Electric Vehicle Council. Ultimately, you will need to analyse the data, interpret the results, and then
draw appropriate conclusions.
The aims of the assignment are to:
• provide you with some examples of the application of data analysis
• test your understanding of the material presented in the relevant topics
• test your ability to analyse data and effectively communicate your results in a language best suited to target
audience
Before attempting the assignment, make sure you have prepared yourself well. At a minimum, please read the
elevant sections of the prescribed textbook and review the learning materials provided in modules 1 and 2 (i.e.,
Topics 1 to 7).
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MIS770 Foundation Skills in Data Analysis Assignment Two (T3, XXXXXXXXXXPage 1
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MIS770 Foundation Skills in Data Analysis Assignment Two (T3, XXXXXXXXXXPage 2
2.1 Scenario
The Australian Electric Vehicle Council wants you to process and analyse the data set and then answer several
questions. The questions you need to answer are contained in the following memorandum.
Assume that your readers do not have an analytics background, so it’s important that you utilise “plain, easy to
understand language” in your answers. If you believe you need to include any technical terms, then you must explain
these in a clear and succinct manner using layman’s terms.
2.2 Memorandum
Date:
To:
From:
Subject:
20 December, 2022
You, Data Analyst
Jane Smith, CEO
Analysis of Electric Vehicle Data
Dear YourName,
Can you please ca
y out an analysis of the Electric Vehicle data (contained in the file EVData.xlsx) and prepare a
eport containing answers to the following questions.
Q1. Summaries of key variables of interest
Can you please provide me with separate summaries of the following variables, just by themselves? In other words,
please investigate each variable individually without reference to any other variable in the dataset.
(a) “Efficiency_WhKm” – average consumption of the battery in watt hours for each kilometer traveled.
(b) “BodyStyle” – style/size of the car.
Q2. Exploring relationships between two variables
(a) I would like to know if there is a link between the top speed of EVs (“TopSpeed_KmH”) and their price
(“Price”). I suspect that the higher the top speed, the higher the price will be, but I’d like to know if this is
actually the case. Therefore, I’d like you to establish from your sample data if there is any relationship
etween these two variables.
(b) I’m also interested to establish if there is a relationship between the drive type (“PowerTrain”) and the style
(“BodyStyle”).
(c) Further, it would be helpful if we knew if the drive type (“PowerTrain”) has any relationship with how
efficient an EV runs (“Efficiency_WhKm”).
Q3. Estimating EV measures
(a) I would like you to estimate how far EVs overall can drive on a single charge (“Range_Km”).
(b) I’m also interested to know if you can estimate the proportion of all EVs which are perceived as larger cars
(i.e., SUVs or Pickups) (“BodyStyle”).
Q4. Claims about EVs
(a) I read somewhere that acceleration (i.e., 0 to 100 km/h) for EVs (“AccelSec”) was 7 seconds.
I think that acceleration is lower than this figure for EVs (they can go from 0 to 100 km/h in less than 7
seconds). Is there any evidence to suggest that this is the case?
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MIS770 Foundation Skills in Data Analysis Assignment Two (T3, XXXXXXXXXXPage 3
(b) Another claim concerned market segments (“Segment”). The claim was that less than 20% of EVs belonged
to Segment D. Can you also check this claim against your survey data?
Q5. Appropriate sample size
Finally, I am concerned that the sample of 92 EVs is too small to provide accurate results as this seems hardly enough
data. If we ever decide to repeat the analysis, I would like to be able to:
calculate approximately the average range (“Range_Km”) to within 15 kilometers.
Therefore, how many EVs would we need to include in the next analysis to satisfy this requirement?
Regards, Jane
3.0 Report Requirements
• Your report must have a cover sheet containing your personal particulars and the Unit details, an executive
summary, introduction and conclusion.
• Your report should be no longer than 3 pages excluding cover sheet, and there is no need to include any
visualisations (i.e., Charts and Tables) or Appendices in the Report.
• The Charts/Graphics and Tables you create are only to be placed in the Data Analysis file (i.e. the Excel
spreadsheet) and not reproduced in the report.
• Your report is meant to be a stand-alone document. That is, readers should be able to read it without looking
at the data analysis. To this end, do not refer to the visualisations as “as you can see from Figure 1 etc”. You
need to interpret your data analysis visualisations for Jane in the report.
• Suggested Word formatting for the report: Single‐line spacing; no smaller that 10‐ point font; page margins
approx. 25mm, and good use of white space.
• Set out the report in the same order as in the originating Memorandum from Jane, with each section
(question) clearly marked.
• Use plain language and keep your explanations succinct. Avoid the use of technical or statistical jargon. As a
guide to the meaning of “Plain Language”, imagine you are explaining your findings to a person without any
statistical training (e.g., someone who has not studied this unit). What type of language would you use in that
case?
• Marks will be lost if you use unexplained technical terms, i
elevant material, or have poor presentation
organisation.
• All Microsoft Excel output associated with each question in the Memorandum is to be placed in the
co
esponding tab in the file EVData.xlsx
3.1 Data Analysis Instructions/Guidelines
In order to prepare a reply to Jane’s memorandum, you will need to examine and analyse the dataset EVData.xlsx
thoroughly.
Jane has asked a number of questions and your data analysis output (i.e., your charts/tables/graphs) should be
structured such that you answer each question on the separate ta
worksheet provided in your Excel document.
There are also five extra tabs in EVData.xlsx and you should use the various templates contained in these tabs in your
“Confidence Interval”, “Hypothesis” and “Sample Size” answers.
In order to effectively answer the questions, your data analysis output needs to be appropriate. Accordingly, you’ll
need to establish which of the following techniques are applicable for any given question:
• Summary Measures (e.g., descriptive statistics, Inc. outlier detection, percentiles).
• Comparative Summary Measures (i.e., descriptive statistics, outlier detection and percentiles for multiple values
of a variable).
• Suitable tables (such as a frequency distribution) and charts or graphics (such as histograms, box plots, pie
charts, ba
column charts, polygons) that will illustrate more clearly, other important features of a variable.
• Scatter Diagrams (used to visually establish if there is a relationship between two numeric variables).
• Cross Tabulations (sometimes called contingency tables), used to establish the relationships (dependencies)
etween two variables (see Additional Materials under Topic 2 – Creating Cross Tabulations in Excel using Pivot
Tables).
• Confidence Intervals. You can assume that a 95% confidence level is appropriate. We use confidence intervals
when we have no idea about the population parameter we are investigating. Additionally, we would use
confidence intervals if we were asked for an estimate. You should use the relevant Excel templates provided in
the dataset and copy them to the applicable question tab.
• Hypothesis Tests. You can assume that a 5% level of significance is appropriate. We use hypothesis tests when
we are testing a claim, a theory or a standard. You should use the relevant Excel templates provided in the
dataset and copy them to the applicable question tab.
• Sample size calculation: You can assume that a 95% confidence level is appropriate. You should include
comparisons for 90% and 99% and a recommendation for the appropriate sample size.
• To answer some questions, you may need to make certain assumptions about the data set we are using.
Mention these in your data analysis, where relevant. There is no need to mention this in the report.
Note: There is an appendix at the end of each chapter of the prescribed textbook which describes the basic Excel steps
associated with that topic. Chapters 1 to 9 are applicable for this assessment.
3.2 Submission
Your completed assignment should be submitted in two separate files:
• Report (Part A): A word document of no more than 3 pages (excluding title/cover page) that must not contain any
charts/tables/graphs. (Note: Do not submit a pdf document in lieu.). Please name your word document
Assignment2_yourstudentid.docx
• Data Analysis (Part B): An Excel document containing separate tabs/worksheets with charts/tables/graphs for
each question. Please note that all interpretations should be presented in your “Report” and the Excel document
should only contain your intermediate analysis and final output. Please (re-)name your Excel document
Assignment2_yourstudentid.xlsx
The assignment is to be submitted to the MIS770 assignment box in CloudDeakin before 8 pm, Thursday 19th
January 2023. Please ensure you include your name and student details in your Word document as well following
the above file naming convention. Failure to follow this convention may lead to a delay in receiving feedback and
marks.
4.1 Faculty of Business and Law Assignment Extension Procedures
Information for students seeking an extension BEFORE the due date
If you wish to seek an extension for this assignment prior to the due date, you need to apply via the online
Assignment Extension Tool in MIS770 unit site. You must provide a reason for the extension as well as your
supporting documentation (e.g., medical certificate) and a draft of your assignment. Not providing both items would
esult in rejection of your request. This needs to occur as soon as you become aware that you will have difficulty in
meeting the due date. To support you in using the tool, the Learning Innovation team has prepared the following
short video: https:
video.deakin.edu.au/media/t/1_g9rtrqow
Students who request an extension due to Covid are required to provide evidence of a positive PCR test or a reply
notification that they have registered a positive RAT test where possible (i.e. for students located in Australia) or
other evidence (for students located offshore). This process needs to occur as soon as you become aware that you will
have difficulty in meeting the due date.
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MIS770 Foundation Skills in Data Analysis Assignment Two (T3, XXXXXXXXXXPage 4
https:
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https:
d2l.deakin.edu.au/d2l/lp/navbars/1192499/customlinks/external/11392
https:
d2l.deakin.edu.au/d2l/home/1192499
https:
video.deakin.edu.au/media/t/1_g9rtrqow
Please note: Conditions under which an extension will normally be considered include:
• Medical – to cover medical conditions of a serious nature, e.g., hospitalisation, serious injury or chronic illness.
Note: temporary minor ailments such as headaches, colds and minor gastric upsets are generally regarded as
not serious medical conditions.
• Compassionate – e.g., death of a close family member, significant family and relationship problems.
• Hardship/Trauma – e.g., sudden loss or gain of employment, severe disruption to domestic a
angements,
victim of crime.
Note: misreading the due date, assignment anxiety or travel will not be accepted as grounds for consideration.
Information for students seeking an extension AFTER the due date
If the due date has passed; you require more than two weeks extension, or