MIS171 Business Analytics Trimester 3 2024

Assessment Task 3 Individual

DUE DATE: Monday, 3 February 2025, by 8:00pm (Melbourne time)

PERCENTAGE OF FINAL GRADE: 40%

SUBMISSION: You will submit to unit site:

• one Excel file, with your analysis, and

• one Word file, with your written report

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 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 3 Data.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 VoltEco charging patterns 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: VoltEco Charging Patterns Analysis

The global transition to electric vehicles (EVs) represents one of the most significant shifts in transportation since the invention of the automobile. As EV adoption accelerates, with global sales doubling in the past two years, the efficiency and reliability of charging infrastructure have become critical factors in supporting this transformation. This study examines the complex interplay of factors affecting EV charging efficiency through the lens of comprehensive charging session data collected by VoltEco.

The challenge of optimising EV charging extends beyond simple power delivery. It encompasses a sophisticated matrix of variables including ambient temperature variations, battery limitations, power grid constraints, and user behaviour patterns. Understanding these relationships is crucial for charging network operators, vehicle manufacturers, and policymakers as they work to create a robust and efficient charging ecosystem.

VoltEco needs to model the charging efficiency based on the independent variables that are available. By analysing this data, the management will gain a deeper understanding of how exactly the charging efficiency vary according to different features. It is possible for them to adapt their business strategy accordingly in order to maximise charging efficiency and to meet the expectations of their customers. The model will also provide management with an insight into the future development of charging networks and the broader adoption of electric vehicles.

This assignment is designed to engage your critical thinking, problem-solving, and analytical skills through the use of predictive analytics on the given dataset. The objective is to conduct a multiple linear regression analysis to explore the factors that potentially contribute to maximising Charging Efficiency. Building upon Assignment 1's interactive dashboard/data visualisation and Assignment 2s descriptive analytics, your challenge is to explore the dataset to uncover meaningful insights and patterns that illustrate the progress made and challenges faced in enhancing Charging Efficiency.

A question, accompanied by guidelines highlighted in blue, are presented below. You are required to submit your Excel file containing your data analysis, along with a report that explains the outcomes of your analysis and two recommendations. Given that your audience may not have training in business analytics, your report must present the results in plain, straightforward language. A template has been provided for your use.

Multiple Linear Regression Modelling (consider ? = 5%)

Charging Efficiency is an important measure for the success of VoltEco, as it represents a major element of the companys marketing strategy. Build a multiple regression model to predict Charging Efficiency. Your model should provide insights into which factors have a significant influence on charging efficiency, as well as the ability to predict charging efficiency for various scenarios.

For this analysis, you will need to build a multiple regression model using Charging Efficiency as the dependent variable. All other variables in the VoltEco dataset should be included in the model, except User ID i.e., exclude User ID from your regression model.

Follow the model building process introduced in the lecture and seminars. Carefully consider the following:

• Transform categorical variables into suitable dummy variables

(i.e., Vehicle Model, Charging Location, Time of Day, Charger Type and User Type).

Copy the VoltEco Dataset to the Correlation spreadsheet in the Excel file that has been provided (no earlier than Column AI - be careful not to overwrite the Conclusion, Correlation Table and Scatter Diagram frames).

1. When transforming Vehicle Model into dummy variables, consider Nissan Leaf as the baseline category; meaning the created dummy variables for Vehicle Model should only include BMW i3 (Yes and No), Hyundai Kona (Yes and No), Tesla Model 3 (Yes and No), and Chevy Bolt (Yes and No).

1. When transforming Charging Station Location into dummy variables, consider Burwood as the baseline category.

1. When transforming Time of Day into dummy variables, consider Morning as thebaseline category.

1. When transforming Charger Type into dummy variables, consider Standard as thebaseline category.

1. When transforming User Type into dummy variables, consider Commuter as thebaseline category.

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.

• Using the VoltEco dataset (which now includes transformed dummy variables) as your reference, complete the following steps:
  
  
  1. Correlation in the section marked Correlation Table (below the Conclusion section on the Correlation worksheet) generate a correlation table. Use the Correlation option in Excels Data Analysis ToolPak.
  
  
  1. 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 -0.050 and 050). These IVs are to be removed from the model prior to running the first iteration of the regression model.
  
  
  1. 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 Charging Efficiency (DV).
  
  
  1. Multi-collinearity - review the correlation table for instances of multi-collinearity between Independent Variables (IV). In cases of multicollinearity (please consider correlation between IVs greater than 0.7 or less than -0.7), identify and clearly indicate the IVs with the weakest correlation with the Dependent These IVs are to be removed from the model prior to running the first iteration of the regression model.
  
  
  1. Complete the Multi-Collinearity summary table which is in the Conclusion section of the Correlation This table summarises which Independent Variables are to be eliminated from the regression model due to multi-collinearity.
  
  
  1. Scatter diagrams - in the section marked Scatter Diagrams (below the Correlation Table section on the Correlation worksheet) generate three scatter diagrams, for:
  
  
  

• Charging Efficiency (Dependent Variable, DV) and the numerical (not dummy 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, and the coefficient of determination.

• Charging Efficiency (DV) and the numerical (not dummy categorical) Independent Variable (IV) which has the highest inverse (i.e., most negative) correlation with the DV. Include a calculation of the correlation coefficient. Format the diagram, and include a linear trendline, and the coefficient of determination.

• Charging Efficiency (DV) and the numerical (not dummy categorical) 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 trendline, and the coefficient of

• 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), complete the following steps:
  
  
  1. Using the Regression option in Excels 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%).
     
     
     
  
  
  1. If your first iteration of the overall model is found to be significant, in a step-wise 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 (i.e., check p values) with alpha set at 0.05 to determine the significance of the various IVs you exclude and include in your
     
     
     
  
  
  

• Once you have created a regression model where all the remaining Independent Variables are contributing significantly to a change in Charging Efficiency, 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,
  
  
  1. In the Conclusion section of the Regression Model spreadsheet,
     
     
     • Write the (final) multiple regression Use the format: ? = ?₀ + ?₁X₁ + ?₂X₂
     
     
     • Explain (interpret) the (final) multiple regression equation/model.
     
     
     
  
  
  

• Using the final multiple regression equation (from the previous step),
  
  
  1. 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 Charging Efficiency (DV),
     
     
     • Calculate a Prediction Interval for Charging Efficiency (DV),
     
     
     • Calculate a Confidence Interval for Charging Efficiency (DV),
     
     
     
  
  
  1. 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 calculation
     
     
     • Interpret the Prediction Interval calculation
     
     
     • Interpret the Confidence Interval calculation

Data description

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

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 (spreadsheets) for:

• Data Description

• VoltEco Charging Patterns Dataset

• Correlation, which includes:
  
  
  • creating dummy variables,
  
  
  • creating correlation table,
  
  
  • eliminating uncorrelated independent variables (IVs), and
  
  
  • eliminating IVs where multi-collinearity is present
  
  
  

• Regression Model building the regression model, including multiple iterations, and
  
  
  • reporting the summary output of the final regression model,
  
  
  • identifying the final equation, and explaining/interpreting the final equation, and
  
  
  • calculating and explaining the point estimate, prediction interval, and confidence interval for the scenario provided.
  
  
  

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 Correlation and Regression Model 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. When drafting your Conclusion, make sure that you directly answer the questions asked. 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 and 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 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. Given that your audience may 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 of the Correlation and Regression analyses. In section 2 of the report, Make TWO (2) recommendations that the VoltEco Board could consider maximising Charging Efficiency. Your recommendations should be based on analysis in this assignment, analysis from previous assignments, and any additional relevant analysis that enhances the impact of your recommendations.

Consider the following in framing your recommendations:

• Specific actions VoltEco could take to maximise Charging Efficiency based on the outcomes of your regression model.

• Specific actions VoltEco could take to maximise Charging Efficiency based on the outcomes of your analysis from Assignment 1 and Assignment 2.

• Specific actions VoltEco could take to maximise Charging Efficiency based on the outcomes of any additional analysis you perform.

• Recommending targeting a group that VoltEco could pursue that maximises Charging

• The impact of other important measures such as Charging Location, Charger Type and User Type on Charging Efficiency.

• Considering the impact on Charging Efficiency of the variables not specifically included in your regression model.

• Recommending strategies for targeting specific Charger Type or Charging Location that could significantly improve Charging Efficiency.

Ensure 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.

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

• Your report is to be written as a stand-alone

• 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 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 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).

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 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 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%

• 2 days late: submitted after Tuesday 11:59pm and before Wednesday 11:59pm 10%

• 3 days late: submitted after Wednesday 11:59pm and before Thursday 11:59pm 15%

• 4 days late: submitted after Thursday 11:59pm and before Friday 11:59pm 20%

• 5 days late: submitted after Friday 11:59pm and before Saturday 11:59pm 25%

• 6 days late: submitted after Saturday 11:59pm and before Sunday 11:59pm 30%

• 7 days 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 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: 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.