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 electronically, 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 accepted.
The assignment uses the file 2024 T3 MIS171 Assignment 2 Data.xlsx which can be downloaded from CloudDeakin. 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 VoltEco dataset. Following is an introduction to this scenario and detailed guidelines.
Context/Scenario: VoltEco 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 VoltEco. You have received an email from Jason Phillips , the CEO of VoltEco. Jasons email asks you to analyse the VoltEco 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:
Categorical Variables
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
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.
Categorical/Categorical Variables Cross-tabulations
You will need to create four cross-tabulation tables (pivot-table format will be accepted) that identifies:
iii. the proportion of Vehicle Models in each Charger Types (% of column total), and
Categorical/Numerical Variables Comparative summary measures
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
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
To answer this question, you will need to do separate probability calculations for each Charging Station.
To answer this question, you will need to do separate probability calculations for each Vehicle Models.
Charging Efficiency is an important measure for VoltEco. 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 VoltEco? 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.
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
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.
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
The assignment consists of two parts.
Your data analysis must be performed on the Assignment 2 Excel file. The file includes tabs for:
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.
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.
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:
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.
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 capabilities |
|
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 |
Your submission will comprise of two files:
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