Description

You are a data analyst in the Research and Analysis group at Financial Review Magazine. Your primary role is to evaluate new products and services. You are often required to report outcomes of your analysis to senior editors at the Magazine who have little or no knowledge of data analysis.

Of specific interest to Financial Review magazine is the increasing number 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 advertise that they can save vehicle owners hundreds of dollars each year on insurance premiums.

Your research and analysis group recently secured a dataset from the Insurance Brokers Association (IBA). It is a random sample of 400 customers who obtained the services of car insurance brokers.

Your Manager – Edmond Kendrick, has asked you to analyse the collected data. In particular, you are expected to perform a series of descriptive and inferential analyses and produce a report based on your findings.

Edmond’s email to you is below.

 

To: <<Your Name>>

From: Edmond Kendrick

Subject: Analysis of car insurance brokerage service

As discussed earlier, I got one of the IT colleagues to clean and simplify the dataset for your convenience. The cleaned dataset contains information about 400 randomly selected customers, and  I have the following questions/issues relating to the insurance brokers data.

1. Do female drivers under 30 save more on car insurance premiums than their male counterparts, on average?
2. Is the proportion of dissatisfied urban customers smaller than the proportion of dissatisfied rural customers?
3. Does the average savings on car insurance premiums differ across the two valuation methods?
4. I would also like you to analyse whether:
   1. The average savings on insurance premiums significantly differ across NSW, Victoria, and Queensland.
   2. The proportion of satisfied customers differ across the insurance brokers.
5. I would like you to design and run an experiment to see the effect of the valuation method and the vehicle type on savings on insurance premium using the data set in the attached Excel File – use Data in the “Experiment” worksheet.

 

1. Guidelines for Assignment Planning and Execution Tables

The purpose of this practical task is to help you keep track of your progress with the assignment and submit it on time. To report how you plan your assignment and turn the plan into action, you must complete the tables provided in dot points as clearly as possible. Remember, effective planning, execution, and completing given tasks on time are essential self-management skills.

Note: Dot point writing requires you to use ‘point form’, not complete sentences.

The assignment planning and execution details should be submitted in the appropriate tables provided. The tables should be in dot points. Before filling in the tables, students are strongly encouraged to watch the pre-recorded workshop called ‘How to plan an assignment and turn the plan into action?’ by the Language and Learning Adviser.

Note: Give the assignment planning and execution file the following name A1_Planning_YourStudentID.docx

2. Guidelines for Data Analysis

Read the case study and questions asked by Edmond carefully. Then spend some time reviewing the data to get a sense of the context. The analysis required for this assignment involves material covered in Module 1, with the corresponding tutorials being a useful guide.

The analysis should be submitted in the appropriate worksheets in the Excel file. Each question from the email should be analysed in a separate tab (e.g. Q1, Q2 … or Q3.1, Q3.2 …). You need to add these extra tabs. Before submitting your analysis, make sure it is logically organised, and any incorrect or unnecessary output has been removed. Marks will be penalised for poor presentation or disorganised/incorrect results, or any unnecessary output.

For all questions in the email, you can assume that:

• 95 % confidence level is appropriate for confidence intervals and;
• 0 % level of significance (i.e. α = 0.05) is appropriate for any hypothesis tests.

You can complete all data analysis using the Excel templates provided in practicals. In choosing the technique to apply for a given question, keep the following in mind:

• Are we dealing with a numerical variable or categorical variable?
• Are we dealing with one sample, two samples or more than a two-sample situation?
• Are we dealing with independent samples or a paired-sample situation?
• Each question must be answered using the most appropriate technique and justify your decision where applicable.
• For relevant questions, please formulate your hypotheses, and state them clearly in both notation and words in your Excel file.
• Even though a question(s) may lead you to inferential techniques, consider conducting a descriptive analysis of the sample data first.

ATTENTION!

• If you have established a difference between two means or proportions, we expect you to estimate and report the difference.
• If you have established a difference between two or more means or proportions, we expect you to follow up with an appropriate multiple comparison procedure.

You may need to make certain assumptions about the dataset to answer some questions. There will be technical/statistical assumptions that you need to make, for example, whether to use an equal or an unequal variance test. You need to consider and incorporate any violations of assumptions such as unequal sample sizes as limitations of your analysis in the report.

Note: Name your Excel file in the following format A1_YourStudentID.xlsx (use a short file name while analysing).3. Guidelines for the Business Report

Once you have completed your data analysis, you need to summarise the key findings for each question and write a response to Edmond in a report format. Your business report consists of four sections: Introduction, Main Body, Conclusion, and Appendices. The report should be around 1,500 words.

Use proper headings (e.g. Q1, Q2 … or Q3.1, Q3.2…) and titles in the report’s main body. Use subheadings where necessary.

• Include relevant Excel outputs, including templates, tables, charts, and graphs in Appendices (appendices are not included in the word count).
• Ensure these outputs in the Appendix are visually appealing, have a consistent formatting style and proper titles (title, axes titles, etc.), and are numbered correctly. Where necessary, refer to these outputs in the main body of the report. If an output, graph or chart is not referred to in the report body, do not include it in your Appendix.
• The introduction begins by highlighting the main purpose(s) of the analysis and concludes by explaining the structure of the report (i.e., subsequent sections). The conclusion should highlight the key findings of the analyses and explain the main limitations (if any).