Reference no: EM133819446

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 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.

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

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. Jason's email asks you to analyse the VoltEco charging patterns data. Get it solved now!

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

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.

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 VoltEco is charging efficiency. 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.

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:
the number of Vehicle Models in each Charger Types,
the proportion of Vehicle Models in each Charger Types (% of row total),
the proportion of Vehicle Models in each Charger Types (% of column total), and
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:
Ambient temperature during the charging session (Temperature) and Charging Efficiency.
Time taken to charge the vehicle (Charging Duration) and Charging Efficiency.
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.

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.

Confidence Intervals:
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.

Hypothesis Testing (consider α = 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.

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
VoltEco 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 Jason's 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.