MIS770 Foundation Skills in Data Analysis – Trimester 3, 2023 Assessment Task 2 – Individual Assignment

DUE DATE:

PERCENTAGE OF FINAL GRADE: WORD COUNT:

Thursday, 18^th January 2024, by 8:00pm (Melbourne time) 30%

2000 Maximum number of words or equivalent

Description

Purpose

This assignment task is aligned to the learning outcomes and skills GLO4 & ULO2, ULO3 required in applying the ideas and concepts introduced in Modules 1 and 2 to undertake Descriptive Measures, Probability Theory, and Inferences to transform raw data into information and knowledge using appropriate data analysis techniques. You will require to prepare a business report that analyses a given dataset and interprets the results to demonstrate understanding of the specific business problems posed, and that offers conclusions and recommendations that address these problems. You will use plain language to report pertinent findings in a fair, neutral and transparent manner, and present compelling evidence to support their findings. By completing this task, you will encounter with some examples of the application of data analysis within an organisation, test your understanding of the material presented in the relevant topics, and your ability to analyse data, and effectively communicate your results in a language best suited to target audience/business professionals.

Context/Scenario

The Australian Electric Vehicle Council wants you to process and analyse a data set based on available information on a sample of electric vehicles (EVs) 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.

Memorandum                                                                                                                                                                        

Date:

To:

From: Subject:

Dear Yourname,

20^th December 2023 You, Data Analyst Jane Stewart, CEO Analysis of Data

Can you please carry out an analysis of the Electric Vehicle data (contained in the file MIS770A2_yourstudentid.xlsx) and prepare a report 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)    “FastCharge_KmH” – charging speed in kilometers per hour.

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 average consumption of the battery of EVs (“Efficiency_WhKm”) and their price (“Price”). I suspect that the more efficient, 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 between 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 style (“BodyStyle”) has any relationship with how efficient an EV runs (“Efficiency_WhKm”).

Q3. Estimating EV measures

a)    I would like you to estimate the overall price of EVs (“Price”).

b)   I’m also interested to know if you can estimate the proportion of all EVs which are perceived as smaller

cars (i.e., Hatchbacks or Liftbacks) (“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?

b)   Another claim concerned market segments (“Segment”). The claim was that less than 30% of EVs belonged to Segment C. 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 10 kilometers.

Therefore, how many EVs would we need to include in the next analysis to satisfy this requirement?

I look forward to your response,

Jane

Specific Requirements

Before attempting the assignment, make sure you have prepared yourself well. At a minimum, please read the relevant sections of the prescribed textbook and review the materials provided in Modules 1 and 2.

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 4 pages excluding cover sheet, and there is no need to, 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, it should be able to be read 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 Microsoft 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, irrelevant material, or have poor presentation/ organisation.

·      All Microsoft Excel output associated with each question in the Memorandum is to be placed in the corresponding tab in the file MIS770A2_yourstudentid.xlsx

Data Analysis Instructions/Guidelines

In order to prepare a reply to Jane’s memorandum, you will need to examine and analyse the dataset

MIS770A2_yourstudentid.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 tab/worksheet provided in your Excel document. There are also five extra tabs in MIS770A2_yourstudentid.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, bar/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) between 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.

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:

Submission

You must submit your assignment in the Assignment 2 Dropbox in the unit CloudDeakin site on or before the due date. Your completed assignment should be submitted in two separate files:

· Report (Part A): A Microsoft Word document of no more than 4 pages (excluding title/cover page) that must not contain any charts/tables/graphs. (Note: Do not submit a pdf or a Pages file in lieu). Please name your word document MIS770A2_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 name your Excel document MIS770A2_yourstudentid.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

Extensions can only be granted for exceptional and/or unavoidable circumstances outside of your control. 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 health).

Applications for extensions after 12 noon on the submission date require University level special consideration and these applications 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 Thursday at 8:00pm is as follows:

·         1 day late: submitted after Thursday 11:59pm and before Friday 11:59pm– 5% penalty.

·         2 days late: submitted after Friday 11:59pm and before Saturday 11:59pm – 10% penalty.

·         3 days late: submitted after Saturday 11:59pm and before Sunday 11:59pm – 15% penalty.

·         4 days late: submitted after Sunday 11:59pm and before Monday 11:59pm – 20% penalty.

·         5 days late: submitted after Monday 11:59pm and before Tuesday 11:59pm – 25% penalty.

·         6 days late: submitted after Tuesday 11:59pm and before Wednesday 11:59pm – 30% penalty.

·         7 days late: submitted after Wednesday 11:59pm and before Thursday 11:59pm – 35% penalty.

In this example, the Dropbox closes the Thursday after 11:59pm AEST time.

Support

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