Reference no: EM133830230
Business Analytics
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.
Learning Outcome 1: Apply quantitative reasoning skills to analyse business problems.
Learning Outcome 2: Create data-driven/fact-based solutions to complex business scenarios.
Learning Outcome 3: Analyse business performance by implementing contemporary data analysis tools.
Learning Outcome 4: Interpret findings and effectively communicate solutions to business problems
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. Do you need urgent help? Get Solution Now!
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 2's 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
Charging Efficiency is an important measure for the success of VoltEco, as it represents a major element of the company's 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).
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 Efficiency.
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 document.
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.