Overview
The purpose of this assignment is to investigate a dataset that has been produced as a result of the survey you conducted on Climate Change. You now need to interrogate the dataset in order to answer questions posed by your client, the Australian Government. Ultimately, you will need to analyse the data, interpret the results, and then draw appropriate conclusions.
Scenario
The Australian Government has previously commissioned you, Ellyse Perry (Chief Analyst at Survey House), to develop a survey to help them gauge the views of the Australian public regarding “Climate Change”. Now, in order to implement possible Climate Change policy initiatives, the Government, through their representative Daisy Pearce (Manager, Public Relations in the Prime Minister’s Office), wants you to interpret the data gathered from your survey and then answer several questions.
Daisy does not have an analytics background, so it’s important that you utilise “plain, easy to understand language” in your answers. If you believe you need to include any technical terms, then you must explain these in a clear and succinct manner.
The questions you need to answer are contained in the following memorandum.
Memorandum
Date: 24th April, 2020 To: Ellyse Perry, Chief Analyst, Survey House From: Daisy Pearce, Manager, Public Relations, PM’s Office Subject: Analysis of Climate Change Survey Data
Dear Ellyse,
Can you please carry out an analysis of the recent Climate Change Survey data (contained in the file ClimateChangeSurvey.xlsx) and prepare a Memorandum reply to me containing answers to the following questions.
My specific questions are:
Q1. An Overall View of “Monthly_Payment” spend per month
Can you provide me with an overall summary of the amount people are willing to pay monthly in order to have a net zero electricity bill sourced entirely through renewable energy (e.g. solar panels with backup battery)? A summing up of the variable “Monthly_Payment” just by itself, would be useful.
Q2. “Monthly_Payment” vs “Concerned”
I am interested in how the “Monthly_Payment” amount people are willing to pay for renewable energy relates to how “Concerned” people are with the current situation regarding Climate Change. In particular, does there appear to be any difference in how much people are prepared to pay monthly on renewable energy compared to the “Concerned” responses of: Not concerned; Slightly concerned; Somewhat concerned; Very concerned, and Extremely concerned.
Q3. Climate Change Affordability Measures
(a) Can you estimate the average “Monthly_Payment” amount people are willing to pay for renewable energy in Australia?
(b) Climate Change is a global concern and there are countries that do not have the financial resources to address their local Climate Change circumstances. Therefore, I’m interested to know if you can estimate the proportion of all Australians who believe that Australia should financially “Support” developing countries in their efforts to address Climate Change.
Q4. Climate Change Views
(a) A previous report published by one of the national daily newspapers indicated that the proportion of Australians who have Already Installed or Plan to Install “Solar_Panels” on their homes is no more than 25%. An industry energy group has responded to this report by claiming that the rate has been understated and the true figure is higher. Can you check this industry group’s claim?
(b) Another point of contention made in the report was the amount of money Australians would be prepared to make an up-front payment (“Up_Front_Payment”) in order to have net zero electricity bills
through renewable energy. The report states that it is, on average, less than $21,500. The same industry energy group has responded by claiming that the amount of money Australians are prepared to pay up-front is at least $21,500. Could you please also check this claim by the industry group?
Q5. Relationships
I would like to see whether factors listed below provide any explanation of the amount people are willing to pay monthly (“Monthly_Payment”) in order to have net zero electricity bills through renewable energy. If so, can you also indicate which factor is the most important?
(a) “Income”
(b) “Age”
Q6. Appropriate Sample Size
Finally, I am concerned that the sample of 400 Australians is too small to provide accurate results as this seems hardly enough data. For a survey we intend to undertake next year, we would like to be able to:
(a) estimate the proportion of Australians that are Aware or Very Aware of Climate Change to within 3%, and
(b) accurately estimate the average monthly payment (“Monthly_Payment”) amount people are willing to pay in order to have net zero electricity bills through renewable energy to within $20.
How many Australians would we need to include in next year’s survey to satisfy both of these requirements?
Regards, Daisy
Memorandum Requirements
• Your memorandum reply should be no longer than 3 pages and there is no need to include a Table of Contents, any visualisations (i.e. Charts and Tables), or Appendices in the Memorandum. The Charts/Graphics and Tables you create are only to be placed in the Data Analysis file (i.e. the Excel spreadsheet) and not reproduced in the memorandum.
• Suggested Word formatting for the memorandum: Single‐line spacing; no smaller that 10‐ point font; page margins approx. 25mm, and good use of white space.
• Your memorandum must, however, have a cover sheet containing your particulars and Unit details.
• Set out the memorandum in the same order as in the originating Memorandum from Daisy, with each section (question) clearly marked.
• Use plain language and keep your explanations succinct. Avoid the use of technical or statistical jargon. As a guide to the meaning of “Plain Language”, imagine you are explaining your findings to a person without any statistical training (e.g. someone who has not studied this unit). What type of language would you use in that case?
• Marks will be lost if you use unexplained technical terms, irrelevant material, or have poor presentation/organisation.
• All Microsoft Excel output associated with each question in the Memorandum is to be placed in the corresponding tab in the file ClimateChange_yourstudentid.xlsx
Data Analysis Instructions/Guidelines
In order to prepare a reply to Daisy’s memorandum, you will need to examine and analyse the dataset ClimateChangeSurvey.xlsx thoroughly.
Daisy has asked a number of questions and your Data Analysis output (i.e. your charts/tables/graphs) should be structured such that you answer each question on the separate tab/worksheet provided in your Excel document. There are also three extra tabs in ClimateChangeSurvey.xlsx called CI, HT and SS and you may use the various templates contained in these tabs in your “Confidence Interval”, “Hypothesis” and “Sample Size” answers should you wish to do so.
In order to effectively answer the questions, your Data Analysis output needs to be appropriate. Accordingly, you’ll need to establish which of the following techniques are applicable for any given question:
• Summary Measures (e.g. Descriptive Statistics, Inc. Outlier detection).
• Comparative Summary Measures (i.e. Descriptive Statistics for multiple values of a variable).
• Suitable tables (such as a Frequency Distribution) and charts or graphics (such as Histograms, Box Plots, Pie Charts, Bar/Column Charts) that will illustrate more clearly, other important features of a variable.
• Scatter Diagrams (used to visually establish if there is a relationship between two numeric variables).
• Cross Tabulations (sometimes called Contingency Tables), used to establish the relationships (dependencies) between two variables (see Additional Materials under Topic 2 – Creating Cross Tabulations in Excel using Pivot Tables).
• Confidence Intervals. You can assume that a 95% confidence level is appropriate. We use Confidence Intervals when we have no idea about the population parameter we are investigating. Additionally, we would use Confidence Intervals if we are asked for an estimate. You can use the relevant Excel templates provided in the dataset and copy them to the applicable question tab.
• Hypothesis Tests. You can assume that a 5% level of significance is appropriate. We Use Hypothesis Tests when we are testing a Claim, a Theory or a Standard. You can use the relevant Excel templates provided in the dataset and copy them to the applicable question tab.
• Sample size calculation: You can assume that a 95% confidence level is appropriate. You may include comparisons for 90% and 99% and a recommendation for the appropriate sample size.
• To answer some questions, you may need to make certain assumptions about the data set we are using. Mention these in your data analysis, where relevant. There is no need to mention this in the memo.