Scenario

You are Alex Cassidy, a business analyst who works for the BLITZ department store chain in their Research and Analysis department. You have been asked by the General Manager, Ms Jacinta Liu, to analyse a random sample of data collected from a recent survey of 300 randomly selected customers shopping in our stores. Her email making this request of you is reproduced below.

Email from the GM

To: Alex Cassidy

From: Jacinta Liu

Subject: Analysis of the BLITZ Department store data

Dear Alex,

Regarding the recent survey of the 300 customers in three major cities of Australia, the marketing department requires answers to the following questions. Your responses will be used as part of a report to senior management comparing customer habits and characteristics across the three cities.

1. Regarding the age of our customers and the amount they spent in our stores.

a. Can you provide a profile of our customers' age please?

b. Is there any evidence to suggest any differences in the average age of customers between the three cities?

c. Generally speaking, can we make the claim that the average amount spent per visit by all our customers is less than $105?

d. Is the amount of money spent per visit influenced by the age of the customer?
2. Regarding the shopping habits of our customers and their usage of the beauty sections within our department stores.

a. There have been some excellent instore promotions recently. Can you provide an estimate of the proportion of customers who shop in the beauty section in each of the three cities?

b. Do we have enough evidence to claim that the proportion who do shop in the beauty sections of all of our stores, could now be more than 48%?

c. Given that some of our customers are female, please indicate the likelihood that they might shop in the beauty section of our department store.

d. Is there any evidence to suggest that a relationship exists between shopping in the beauty sections of our department stores and whether or not a person is male or female?
I look forward to your response. Sincerely,
Jacinta Liu General Manager

Part 1: Data Analysis
In order to prepare a reply to the General Manager, you will need to examine and analyse the dataset thoroughly. The General Manager has asked a number of questions.
For all relevant questions in the email, you can assume that:
- a 95% confidence level is appropriate for confidence intervals and
- a 5% level of significance (that is, alpha (α) = 0.05) is appropriate for hypothesis tests.

The following guidelines for each question should be considered carefully:
Q1. Regarding customers' age and the amount of money spent on a visit.

a. You will need to produce the relevant summary statistics and suitable table(s) and graph(s).

b. Firstly, you will need to produce the relevant summary statistics. Once done, you should then use an appropriate inferential technique to determine if there is any difference in the average age of customers between the three cities.

c. Here, you will again need to generate the relevant summary statistics. Once done, you should then use an appropriate inferential technique to answer the question whether or not the average amount spent by all customers is less than $105.

d. You will need to use relevant relationship technique(s) to see whether the amount of money spent per visit is influenced by age of the customer.
Q2. Regarding the shopping habits of our customers and their usage of the beauty sections within our department stores.

a. After producing the relevant summary statistics, you will need to use an appropriate inferential technique to estimate the proportion of shoppers who are shopping in the beauty sections in each of the three cities.

b. Produce the relevant summary statistics. You will then need to use an appropriate inferential technique to determine if the proportion of all shoppers shopping in the beauty section could actually be greater than 48%.

c. This question is conditional upon the customer being a female. To assist in solving this question and the question below it (d.), you should create cross tabulations for the variables Beauty and the Gender.

Part 2: Email
You are required to reply by email, detailing essential information and conclusions from your data analysis. You are allowed no more than 2 pages to convey your written conclusions.
Keep the English simple and the explanations succinct. Avoid the use of technical statistical jargon. Your reader will not necessarily understand even simple statistical terms, thus your task is to convert your analysis into plain, simple, easy to understand language.
Further IMPORTANT instructions:
- The email is to be written as a stand-alone document (assume that the Jacinta Liu will only read your email). Thus, you should not have any references in the email to your analysis, nor should you include any charts and tables within it.
- Use an email format for your reply. That means the email heading (eg. To:, From:, Subject:) should be included, the recipient should be addressed at the beginning and the signature or name of the sender should be included at the end.
- When composing your reply, make sure that you actually answer the questions asked. Cite (state) the summary statistics of importance without referring to your analysis section.
- Sequentially number your answers in both your email and your analysis (1, 2 ... 5) to match the General Manager's email.
- Include a simple introduction at the start of the email and a summary/conclusion at the end.
- Marks will be deducted for the use of technical terms, irrelevant material, poor presentation/organisation/formatting and emails that are over two pages long.

When you have completed the email, it is a useful exercise to leave it for a day, return to it and re-read 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 on re-reading, you become aware that you have made some points in a clumsy manner, and you find that you can re-phrase them much more clearly.

Attachment:- Data.rar