MIS171-Analyses Data Set & RACV Solar Case Study – IT Computer Science Assignment Help

Assignment Task

Task 

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 email. 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 2022 T1 RACV Solar Data Set A2.xlsx which can be downloaded from CloudDeakin. The assignment focuses on materials presented up to and including Week 6. The data set is based on actual data provided by RACV Solar. For confidentiality and anonymity reasons actual data has been manipulated in the assessment task. Following is an introduction to this scenario and detailed guidelines. 

RACV Solar
RACV Solar is a wholly-owned subsidiary of the RACV (Royal Automobile Club of Victoria). While the RACV is best known as a provider of car and road-user services, the organisation has been active in expanding the range of services it offers to its members, including insurance and travel services.
RACV Solar is one of the biggest solar companies in Victoria. RACV Solar has designed and installed more than 10,000 solar installations, with over 50 MW (megawatts) of solar power across multiple sectors, including residential homes, businesses, and community projects.
RACV Solar holds the record for the largest installations on a school and a hospital in Australia.

Scenario: RACV Community Solar Installation Promotion
In 2021, RACV Solar partnered with Geelong Sustainability to offer solar power generating and battery systems to households in the Geelong, Surf Coast and Otway region. Key data from 250 clients who participated in the promotion is included in the data set in the Excel file which forms part of this assignment (2022 T1 RACV Solar Data Set A2). As a commercial business RACV Solar is keen to convert an enquiry from a potential client into a confirmed commitment to purchase a solar installation. RACV Solar aspires to minimise the time between first enquiry and confirmed commitment (i.e., “Conversion Period”).

The promotion with Geelong Sustainability provided potential clients with multiple points of contact. The main pathways were coming into an RACV Store or via the RACV Solar website. However, clients also made enquiries via RACV Solar’s social media platforms (e.g., Facebook and Instagram) or by referral from other clients.
Every client enquiry was managed by one of RACV Solar’s three salespeople – Anand, Clayton, or Daiyu. The performance of RACV Solar’s salespeople is measured according to time to “close the deal”, revenue generated (i.e., System Cost”), and client satisfaction.

The size of a solar electricity system is measured in kilowatts (kW). This represents the capacity of a solar installation at maximum efficiency. Larger systems have greater capacity to generate electricity than smaller systems. The amount of electricity generated depends on several factors, including system size, orientation (e.g., if the system faces north or west), and shading (i.e., if any shade, for example from nearby trees, falls on the solar panels during the day). Electricity generated is measured as kilowatt hours (kWh), the number of hours the system will power appliances with a requirement of 1,000 watts (i.e., one kilowatt).
The cost of the installation for the client is revenue for RACV Solar. Cost depends on several factors, including the size of the system, the complexity of the system (e.g., one bank of panels facing north and another bank of panels facing west), access to the site, and other factors (e.g., clients are offered different models of solar panels and extended warranties).

1. Univariate Analysis:
Categorical Variables
1.1. Provide a profile of the categorical variable Method of Enquiry. We want to know:

• whether there was an even spread of clients across all Methods of Enquiry; and
• which was the most effective (and least effective) Method of Enquiry (the most effective method will be the one with the highest proportion of clients accepting a quote from RACV Solar).

You will need to create a suitable table which includes the number and proportion of clients from the different Methods of Enquiry Create an appropriate graph to illustrate your analysis.

Numerical Variables
1.2. A key promotional tool for RACV Solar is size of the solar energy systems we install. Provide an analysis of the System Size. Provide a summary of the THREE most significant observations from your analysis.
You will need to generate the appropriate Descriptive/Summary Statistics for System Size. Also include quartile details, and the interquartile range. Using an appropriate technique, determine whether or not there are any outliers.Create an appropriate graph to illustrate your analysis.

2. Bivariate Analysis:
2.1. We are interested in knowing if focussing each sales person in a specific location will make them more effective. Provide an analysis of the deals each Sales Person closed in each Location. Our view is that if any sales person closes more than 40% of the deals in any location this indicates superior effectiveness in that location. Closing fewer than 30% of the deals in any location indicates limited effectiveness in that location. Specifically:

• Provide THREE key observations from your Sales Person:Location analysis
• Advise if the data indicates if any sales person has demonstrated superior effectiveness in any location
• Advise if the data indicates if any sales person has limited effectiveness in any location

You will need to create the appropriate (pivot) table(s) and/or heat map(s) that identifies the number (and proportion) of deals closed by each sales person in each location. In your analysis, include the overall number (and proportion) of deals closed by each sales person.

2.2. We believe that the annual payoff from installing a solar system will be a key driver of future  business. Also, because many people considering installing a solar energy system are motivated by the experience of friends and family we believe that maximising client satisfaction is another important generator of future business.
Provide an analysis of the Annual Payoff for the different Satisfaction Types:

• Provide THREE key observations from your Annual Payoff : Satisfaction Type analysis
• Does the data indicate a relationship between satisfaction and annual payoff?

For the variable Satisfaction Type, you will need to create a new column in the RACV Solar Data spreadsheet headed “Client Satisfaction Type”. Using the information in the Data Description tab, you will need to categorise each client according to their Satisfaction Type (categorical) based on their Client Satisfaction (numerical).

Please consider creating an additional categorical variable, for Payoff Type:
Low                     clients with an annual payoff <$1,000
Modest              clients with an annual payoff between $1,000 and $1,999
Reasonable       clients with an annual payoff between $2,000 and $2,999
Substantial       clients with an annual payoff between $3,000 and $3,999
Highest              clients with an annual payoff of $4,000 or more

You will need to create a (pivot) table that indicates the number and proportion of clients in each Satisfaction Type, and (at least) the respective average payoff.
Create Satisfaction Type:Payoff Type cross-tabulation tables with the number (and proportion) of clients in each category. Consider a heatmap approach to the tables.
Create appropriate graphs to illustrate your analysis.

2.3. Are there any relationships between the following:
a) System Size and System Cost.
b) System Size and Annual Payoff.
c) Client Satisfaction and Annual Payoff.

You will need to calculate suitable association measures.
Create appropriate graphs to illustrate your analysis

3. Probability:
3.1. Assuming that the Annual Payoff is approximately normally distributed, for each Location, what is the probability that Annual Payoff would be less than $1,000?
Advise which Location has the highest probability of an Annual Payoff less than $1,000, and which Location has highest probability of an Annual Payoff more than $1,000.
To answer this question, you will need to do separate probability calculations for each Location.

3.2. Assuming that the System Cost (i.e. Client Revenue) is approximately normally distributed, for each Sales Person, what is the probability that System Cost exceeds $7,500? Advise which Sales Person has the highest probability of generating a client sale that is more than $7,500, and which Sales Person has the highest probability of generating a client sale that is less than $7,500 To answer this question, you will need to do separate probability calculations for each Sales Person.

3.3. Assume that Client Satisfaction is approximately normally distributed. For each Method of Enquiry, what is the Client Satisfaction score that 80% of clients are below (and 20% are above)?
To answer this part of the question, you will need to do separate proportion calculations for each Method of Enquiry. Advise which Method of Enquiry typically has the lowest relative variability of Client Satisfaction, and which Method of Enquiry typically has the highest variability of Client Satisfaction.
To answer this part of the question calculate the coefficient of variation for each Method of Enquiry.

4. Confidence Intervals:

4.1. Conversion Period is an important measure for RACV Solar. Please provide an overall estimate of the average Conversion Period for clients of each Gender. Which gender appears to be the fastest to accept the quote from RACV Solar? Which gender appears to take the longest time to accept the quote from RACV Solar?
You will need to produce a comparative table of descriptive/summary statistics for the Conversion Period for each Gender. Then, you will need to calculate a 95% confidence interval for average Conversion Period for each Gender. Create an appropriate graph to illustrate your analysis.

4.2. We consider that client satisfaction is a key driver of future business for RACV Solar. Our goal is to have 25% of clients in each of the (four) different Satisfaction Types. Does the data indicate that we are meeting our goal? If not, is there any group that is represented more than the others?
You will need to use the categorical variable “Satisfaction Type” created earlier (Question 2.2), which was based on the information provided in the Data Description sheet. You will need to produce a (pivot) table detailing the number (count) and proportion of each Satisfaction Type. Then, you will need to calculate, compare and contrast, 95% confidence interval estimates for the proportion of clients of the different Satisfaction Types. Create an appropriate graph to illustrate your analysis.

Data description
The provided data file includes multiple sheets, labelled “Data Description”, “RACV Solar Data Set” and a worksheet for each question. The “Data Description” sheet describes all the variables used in the “RACV Solar Data Set” and is copied below for your convenience.

Part 1: Data Analysis
Your data analysis must be performed on the Assignment 2 Excel file. The file includes tabs for:
• Data Description
• RACV Solar Data Set
• Analysis for questions 1, 2, 3, and 4

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 Q4 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 James’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 for your analysis, which follows the direction provided in James’s email, and supports your response to his 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. 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 which can be placed before the RACV Solar Board of Directors. The report must explain all four (4) analysis questions (Univariate Analysis, Bivariate Analysis, Probability, and Confidence Intervals). Assume that none of the directors on the Board have any training in statistics; they 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.

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.
It is useful to produce both numerical and graphical statistical summaries. Sometimes something is revealed in one that is not obvious in the other.

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

• Your report is to be written as a stand-alone document. Assume that your Excel file is for James’s use only and that James will only pass your written report directly to the Board. 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.
• Include a succinct introduction at the start of your report, and a conclusion at the end.
• Marks will be deducted for the inclusion of irrelevant material, poor (unprofessional) 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 rereading, you become aware that you can edit the report to make it more direct and clearer.

Learning Outcomes
This task allows you to demonstrate achievement towards the unit learning outcomes (ULOs). The ULOs are aligned with specific graduate learning outcomes – that is, the skills and knowledge graduates are expected to have upon completion of their studies – and this assessment task is an important tool in determining achievement of those outcomes. If you do not demonstrate achievement of the unit learning outcomes, you will not be successful in this assignment. It is good practice to familiarise yourself with the ULOs and GLOs as they provide guidance on the knowledge, understanding and skills you are expected to demonstrate upon completion of the unit. In this way they can be used to guide your study.

ULO1: Apply quantitative reasoning skills to analyse business problems.
ULO2: Create data-driven/fact-based solutions to complex business scenarios.
ULO3: Analyse business performance by implementing contemporary data analysis tools.
ULO4: Interpret findings and effectively communicate solutions to business problems

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