Reference no: EM132365196

Foundation Skills in Data Analysis

Graduate Learning Outcome

GLO4: Critical thinking: evaluating information using critical and analytical thinking and judgment

Unit Learning Outcome
ULO2: Manipulate and summarise data that accurately represents real world problems
ULO3: Interpret and appraise statistical output to assist in real-world decision making

Overview

The purpose of this assignment is to investigate a dataset utilising the knowledge learned in Modules One and Two. This will enable conclusions to be drawn that ultimately assist in decision making.
The assignment requires you to analyse a given dataset, interpret the results, and then draw conclusions such that you are able to reply to specific questions being asked of you in the form of a report. (These questions are asked in the following memorandum).
The aims of the assignment is to:
• provide you with some examples of the application of data analysis within an organisation
• test your understanding of the material in the relevant topics
• test your ability to analyse and interpret your results
• test your ability to effectively communicate the results of your analysis toothers

Scenario

Emma Thomson, a data analyst in the Research and Analysis Group at Financial Review Magazine is often required to report outcomes of data analysis to senior editors at the Magazine. Primary role of the Research and Analysis Group is to evaluate new financial products and services and provide advice on matters affecting the company.

Of specific interest to Financial Review magazine is the increasing numbers of companies that offer brokerage services for car insurance and potentially what this means for consumers. An insurance broker is an independent insurance agent who works with many insurance companies to find the very best available policies for his or her customers. Most of these brokers are advertising that they are able to save owners of motor vehicles hundreds of dollars each year on insurance.

Paul Anderson, one of the senior editors of Financial Review Magazine has written to you, Emma Thomson regarding the recently purchased dataset concerning car insurance brokerage services. His memo is reproduced below:

Subject: Analysis of car insurance brokerage services

Dear Emma,

Regarding the recently purchased dataset, can you please complete the following for me? Your responses will assist me in writing about Car Insurance Brokerage in the innovative business feature section of our next issue.

1. Can you provide me with an overall summary of how much brokers have saved consumers on annual car insurance premiums?

2. I am also interested in the performance of individual brokers i.e. ability of the insurance broker to save on the car insurance premium. Are there substantial differences in their performance?

3. How satisfied are customers with the brokering service? In addition, can you further analyse to see whether factors such as gender (Male vs. Female) and area they live in (Rural vs. Urban) affect how customers rate their brokering services?

4. I would like to see whether factors listed below provide any explanation in the variation of current insurance premiums. A short description of the relationship would be fine.
• Car Age
• Car Value
• License Held (Years)
• Excess
I realise that the survey relates to a random sample of just 400 customers, and that this information can be used to draw inferences about all customers who use the service of brokerage services to find car insurance products for them. With that in mind, I hope you are able to provide me with answers to the following questions:

5. Can you provide an estimate of the average saving of car insurance premiums for all customers?

6. Can you provide me with an estimate of the proportion of all customers who are worse off (i.e. loss making) using a broker?

7. Some prominent business commentators argue that the average savings on car insurance premiums has gradually declined during the last two years. The National Insurance Brokers Association's report two years ago stated that the average savings on car insurance premium is around $260. Is there any evidence to suggest that the average savings on car insurance premiums has decreased?

8. A recent study suggests that males are less likely to seek the services of an insurance broker than females. The study asserts that the proportion of males that use car insurance brokers is no more than 40%. Can you check whether this claim can also be substantiated?

9. Finally, for a follow up article, I wish to sample enough customers to estimate the average amount brokers save on car insurance premiums to within $50 and the proportion of comprehensive insurance policies to within 5%. How many customers would we need to survey in order to satisfy both of these requirements?

Requirements:
• Your report should be no longer than 2000 words and there is no need to include, Charts and Tables, or Appendices in the report
• Your Charts/Graphics and Tables are only to be placed in the Data Analysis file i.e. the Excelspreadsheet
• The report is to be written as a stand-alone document (assume Paul will only read your report). Thus, you should not have any references in the report to your data analysis output. Eg. "According to Table 1 in the analysis..."
• Your report must have an informative title
• Your report must contain an executive summary that explains in plain language what the report is for and summarises the main findings. The executive summary should be no more than a page
• The body of your report must be set out in the same order as in the originating memorandum from Paul Anderson, with each section (question) clearly marked
• Use plain language and your explanations succinct. Avoid the use of technical or statistical jargon as Paul Anderson will not necessarily understand statistical terms. 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 this case?
• Marks will be lost if you use unexplained technical terms, irrelevant material, or have poor presentation/ organization
• All Microsoft Excel data analysis output associated with each question in the Memorandum is to be placed in the corresponding tab in the yourstudentid.xlsx file

In order to prepare a reply to Paul's memorandum, you will need to examine and analyse the dataset
CarInsuranceData.xlsx thoroughly.
Paul has asked a number of questions and your Data Analysis output (i.e. your charts/tables/graphs) should be structured such that each question is answered on the separate tab/worksheet provided in your Excel document. There are also extra tabs in CarInsuranceData.xlsx called CI, SampleSize and HT and you should use the various templates contained in these tabs in your "CI_Mean/CI_Proportion", "SampleSize" and "HT_Mean/HT_Proportion" answers.

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 each question:
• Summary Measures (Descriptive Statistics, Inc. Outlier detection)
• Comparative Summary Measures (i.e. Descriptive Statistics for multiple values of avariable)
• Suitable tables and charts or graphics (Module One) that will illustrate more clearly, other important features of a variable
• Scatter diagrams, Correlation analysis and Cross Tabulations (sometimes called Contingency Tables), used to establish the relationships (dependencies) between two variables
• 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 to provide an estimate of a population parameter.
• We Use Hypothesis Tests when we are testing a Claim, a Theory or a Standard. Use 5% significance in any hypothesis tests you perform, and provide a summary of your conclusions. Where appropriate, make comparisons with other levels of significance (2%, 1%).
• 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.

Attachment:- Foundation Skills in Data Analysis.rar