Business Analytics Trimester 3, 2024 MIS171

MIS171 Business Analytics - Trimester 3, 2024 Assignment 2 Individual

DUE DATE: Friday, 10 January 2025, by 8:00pm (Melbourne time)

PERCENTAGE OF FINAL GRADE: 40%

SUBMISSION: You will submit via the Unit Site:

• one Excel file, with your analysis, and
• one Word file, with your written report (1,000 words)

Description

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 Cloud Deakin by the due date. When submitting electronically, you must check that you have submitted the work correctly by following the instructions provided in Cloud Deakin. Hard copies or assignments submitted via email will NOT be accepted.

The assignment uses the file 2024 T3 MIS171 Assignment 2 Data.xlsx which can be downloaded from Cloud Deakin. The assignment focuses on materials presented up to and including Week 7. The Excel file which has been provided has different worksheets explaining and containing the Volt Eco dataset. Following is an introduction to this scenario and detailed guidelines.

Context/Scenario: Volt Eco Charging Patterns Analysis

Global adoption of electric vehicles (EVs) is increasing, so understanding the dynamics of charging behaviour, energy consumption, and factors influencing charging efficiency is becoming increasingly important. The charging efficiency, which is the proportion of the energy provided by the charging station that is effectively stored in the electric vehicle's battery, is an important consideration for consumers and energy providers. It is possible for poor charging efficiency to result in increased energy costs as well as a strain on the electrical grid. Therefore, it is essential to understand the factors that contribute to charging efficiency and develop strategies to enhance it.

Assume that you are a business analyst recruited by Volt Eco. You have received an email from Jason Phillips , the CEO of Volt Eco. Jasons email asks you to analyse the Volt Eco charging patterns data.

Your response will be used as part of a report to the VoltEco Board of Directors. Jasons email together with guidelines (shown in blue) are presented below:

Email from Jason Phillips

To: You

From: Jason Phillips, CEO, Volt Eco

Subject: Analysis of the Volt Eco charging patterns dataset

Hi ,

The Board wants a detailed understanding of some of the key aspects of the VoltEco charging patterns. 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 analysis, 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 analysis, your report must present the results of your analysis in plain, straight-forward business language. I have provided a template for you to use.

1. Univariate Analysis:

Categorical Variables

• Provide a profile of the categorical variable Vehicle Model.

Our presumption is that there was an even spread (different proportions) across all vehicle models. If there was an uneven spread of across all vehicle models, advise which was the most frequent (and least frequent) vehicle models.

You will need to create a suitable table that includes the number and proportion of vehicle models.

Create an appropriate graph to illustrate your analysis.

Numerical Variables Descriptive summary measures

• A key measure for the Volt Eco is charging Provide an analysis of Charging Efficiency. Provide THREE significant observations from your analysis.

You will need to generate the appropriate Descriptive/Summary Statistics for Charging Efficiency. Also include quartile details, and the interquartile range. Using an appropriate technique, determine whether or not there are any outliers.

Create an appropriate graph(s) to illustrate your analysis.

2. Bivariate Analysis:

Categorical/Categorical Variables Cross-tabulations

• We are interested to understand more about the charging patterns, and any potential relationship between Vehicle Model and Charger Type. We need you to provide THREE key observations from your analysis.

You will need to create four cross-tabulation tables (pivot-table format will be accepted) that identifies:

1. the number of Vehicle Models in each Charger Types,
   1. the proportion of Vehicle Models in each Charger Types (% of row total),

iii. the proportion of Vehicle Models in each Charger Types (% of column total), and

1. the proportion of Vehicle Models in each Charger Types (% of grand total). Apply the appropriate conditional heat-map formatting to each cross-tabulation.

Categorical/Numerical Variables Comparative summary measures

• We are interested to understand more about charging patterns on time segment, and any potential relationship between Charging Efficiency and Time of Day. We need you to record some key observations from your analysis in the provided table (in the Excel file).

In order to determine the charging efficiency in each time segment, you will need to create appropriate (pivot) table(s) and/or heat map(s).

Create appropriate graphs to illustrate your analysis.

Numerical/Numerical Variables Scatter diagrams and correlation coefficients

• We believe that charging efficiency may be influenced by or correlated with a number of other factors. Specifically, we aim to understand the relationships between the following:

1. Ambient temperature during the charging session (Temperature) and Charging Efficiency.
2. Time taken to charge the vehicle (Charging Duration) and Charging Efficiency.
3. Total energy consumed during the charging session (Energy Consumed) and Charging Efficiency.

You will need to calculate suitable association measures to advise on the nature of these relationships, if any.

Create appropriate graphs to illustrate your analysis.

3. Probability:

• Assuming that the Charging Efficiency is approximately normally distributed, advise which Charging Station has the highest probability of Charging Efficiency exceeding 12.5%.

To answer this question, you will need to do separate probability calculations for each Charging Station.

• Assuming that the Charging Efficiency is approximately normally distributed, advise which Vehicle Model has the lowest probability of Charging Efficiency less than 10%.

To answer this question, you will need to do separate probability calculations for each Vehicle Models.

4. Confidence Intervals:

Charging Efficiency is an important measure for Volt Eco. Please provide an overall estimate of the average charging efficiency for each Vehicle Model. Which model appears to generate the highest (average) charging efficiency for Volt Eco? Which vehicle model appears to generate the lowest (average) charging efficiency for VoltEco?

You will need to produce a comparative table of descriptive/summary statistics of the charging efficiency for each vehicle model. Then, you will need to calculate a 95% confidence interval for average charging efficiency for each vehicle model.

Create an appropriate visualisation to illustrate your analysis.

5. Hypothes is Testing (consider ? = 5%):

It is suggested that the average Charging Efficiency for each Vehicle Model may now be above 10%. Does the data confirm this hypothesis?

To address this question, you will need to conduct an appropriate hypothesis test for the Charging Efficiency percentages for each Vehicle Model.

I look forward to receiving details of your analysis, and your report, by Friday 10 January, 2025. Sincerely,

Jason

Data description

The provided data file includes multiple sheets, labelled Data Description, VoltEco Data and worksheets for the questions. The Data Description sheet describes all the variables used in the VoltEco Data and is copied below for your convenience.

Campaign Sheet:

Variable Name Variable Description

User ID Unique identifier for each user

Vehicle Model The specific EV model being charged (e.g., Tesla Model 3, Nissan Leaf) Battery Capacity (kWh) The total energy storage capacity of the EV's battery

Charging Station Location The location of the charging station (Ballarat, Bendigo, Geelong, etc.)

Energy Consumed (kWh) Total energy consumed during the charging session Charging Duration (hours) Time taken to charge the vehicle

Charging Rate (kW) The average power delivery rate during charging

State of Charge (Start %) Battery percentage at the start of the charging session State of Charge (End %) Battery percentage at the end of the charging session Charger Type Type of charger used (Standard, Enhanced, DC Fast Charger) Temperature (C) Ambient temperature during the charging session

Time of Day Time segment when the charging occurred (morning, afternoon, evening, or night)

Vehicle Age (years) Age of the electric vehicle, measured in years

User Type Classification of user based on driving habits (commuter, casual or long- distance traveller)

Charging Efficiency (%) How much of the energy supplied during charging is actually stored in the battery

Charging Efficiency Bands Charging efficiency is classified into four different categories based on charging efficiency percentage

• Low (less than 00)
• Acceptable (between 00 and 9.99)
• Superior (between 10 and 99)
• Outstanding (more than 00)

Assignment instructions

The assignment consists of two parts.

Part 1: Data Analysis

Your data analysis must be performed on the Assignment 2 Excel file. The file includes tabs for:

• Data Description
• Volt Eco Data
• Analysis for questions 1, 2, 3, 4, and 5

When conducting the analysis, you need to apply techniques from descriptive analytics, visualisations, probabilities, and confidence interval calculations. You will need to use the appropriate (pivot and other) tables, graphs, and summary measures.

The analysis section you submit should be limited to the Q1 to Q5 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 Jasons 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 to complete the analysis as directed and which supports your response to the questions (which you will write in the Conclusion section). Analysis in the Output section will be marked, please make sure your analysis is complete, clear, and easy to follow. You may need to add rows or columns to present your analysis clearly and completely.

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. Given that your audience does not have training in business analytics, your report must present the results in plain, straightforward language. The audience 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 , provide a brief interpretation of your findings for each question. In section 2 of the report , explain whether the company is meeting its goal of average charging efficiency in each vehicle model exceeding 10% (i.e. Superior or Outstanding). In drafting your report, you must draw on and explain the outcome of your analysis. We expect all reports to provide a direct answer to the question of whether or not the project is meeting its goal. The best reports will explore this more deeply and identify the circumstances in which the goal is, and is not, being met.

Do not provide any recommendations.

You are allowed approximately 1,000 words (950 to 1,050 words) for your report (Section 1 and Section2). Remember you should use font size 11 and leave margins of 2.54 cm.

A template is provided for your convenience. Carefully consider the following points:

• Your report is to be written as a stand-alone Assume that your Excel file is for Jasons use only and that Jason will only pass your written report directly to the Board.
• Keep the English simple and the explanations 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 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 your findings.
• 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 re- reading, 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:

Submission

You must submit your assignment in the Assignment Dropbox in the unit Cloud Deakin 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).

When uploading your assignment, your submission files should be named:

Word file: MIS171_T3_YOURStudentID.doc (or .docx), and Excel file: MIS171_T3_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 Cloud Deakin 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 Cloud Deakin 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 Cloud Deakin 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 Student Connect in your Deakin Sync site.

Late submission

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:

• 1day late: submitted after Monday 11:59pm and before Tuesday 11:59pm 5%
• 2days late: submitted after Tuesday 11:59pm and before Wednesday 11:59pm 10%
• 3days late: submitted after Wednesday 11:59pm and before Thursday 11:59pm 15%
• 4days late: submitted after Thursday 11:59pm and before Friday 11:59pm 20%
• 5days late: submitted after Friday 11:59pm and before Saturday 11:59pm 25%
• 6days late: submitted after Saturday 11:59pm and before Sunday 11:59pm 30%
• 7days late: submitted after Sunday 11:59pm and before Monday 11:59pm 35% The Dropbox closes the Monday after 11:59pm AEST/AEDT 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 Smart Thinking 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: http://www.deakin.edu.au/students/study-support

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.