Learning Outcomes:
1: Apply quantitative reasoning skills to analyze business problems.
2: Create data-driven/fact-based solutions to complex business scenarios.
3: Implement contemporary data analysis tools to analyze business performance.
4: Interpret findings and effectively communicate solutions to business scenarios.
Scenario:
You are Priya Acharya, and you work as an analyst for a financial institute named Financero1, which provides its customers with a range of financial services including retail, business and institutional banking, funds management, and investment. Financero decided to conduct research on the saving and shopping habits of its customers. The provided data set includes a random sample of 200 businesses.
You have received an email from the Director of Analytics (Boris Johns), that contains specific questions that you must answer.
Email from the Director of Analytics
To: Priya Acharya
From: Director of Analytics, Boris Johns Subject: Analysis of the provided data set
Dear Priya,
In an effort to develop a greater understanding of the saving and shopping habits of our customers like the ones captured in the sample, we would like some information. Regarding the data you recently received, please provide us with the answers to the following questions.
1. Total Spending per month is the most important measure that Financero is interested in. Can you provide us with an overall estimate of the average Total Spending for all customers? You will need to produce the relevant tabulated summary statistics and graph(s). Then, you will need to calculate a 95% confidence interval for average Total Spending.
2. Are there any differences in the overall proportion of customers having any of the five different Credit Cards (consider None as a group)? That is, is there any one group sampled that proportionally, is represented more so than the others? You will need to produce the relevant tabulated summary statistics and graph(s). Then, you will need to calculate, compare and contrast, 95% confidence interval estimates for the proportion of customers having each type of Credit Card including None, Bronze, Silver, Gold, and Platinum.
3. Are there any differences in the estimate of the average Eating Out spending between customers within different Age Bands? You will need to create a variable called "Age Band" by converting the numerical variable "Age" to a categorical grouping measure based on the information provided in the Data Description sheet. Then, you will need to create suitable cross-tabulation(s) and graph(s). Further, you will need to calculate, compare and contrast, 95% confidence interval estimates for the average Eating Out spending for each Age Band.
4. Are there any relationships between the following:
a. Monthly income and how much people save each month
b. Monthly income and how much people spend on eating out monthly.
c. Monthly income and how much people are spending on their groceries each month.
You will need to calculate suitable association measures and create relevant graph(s).
5. Assuming that the Income for every Age Band is approximately normally distributed, answer the following questions for each Age Band separately:
a. What is the probability that Income exceeds $4,500?
b. What is the probability that Income would be less than $3,500?
c. What is the value of Income for each Age Band, such that only 10% of that particular age band will achieve it?
To answer this question, you will need to do the probability calculations for each Age Band separately.
6. a. It has been suggested at our most recent meeting that the average Total Spending (per month) of every Age Band, is now more than $4,200. Does this data confirm this hypothesis?
b. Further, is there sufficient evidence to conclude that the proportion of customers in any Age Band is below 25% of all customers?
To answer this question, you will need to conduct appropriate hypothesis testing for each Age Band separately.
Part 1: Data Analysis
When conducting the analysis, you will apply techniques from descriptive analytics, visualisations, probabilities, hypothesis testing, and confidence interval calculation. Hence, you will use various tables, graphs, and summary measures. When exploring data, we often produce more results than we eventually use in the final report, but by investigating the data from different angles, we can develop a much better 'feel' for the data: a deeper understanding of the data. Always ensure that you consider relevant modelling assumptions.
The analysis section you submit should be on Q1 to Q6 sheets of the Excel file. Where possible, it is always useful to produce both numerical and graphical statistical summaries as sometimes, something is revealed in one that is not obvious in the other. Your analysis should be clearly labelled and grouped around each question. Poorly presented, unorganised analysis, or excessive output will be penalised.
Only use cells B2 to Z26 for the results and the rest of the sheets for calculations. Only the information that you present in these cells RANGE (B2:Z26) will be marked.
Part 2: Email
You are required to reply by email, detailing all essential information and relevant conclusions from your data analysis. You are allowed no more than 2 pages to convey your written conclusions. Remember you should use font size 11 and leave a margin of 2.54 cm. Please consider the following dot points very carefully.
- Keep the English simple and the explanations succinct. Avoid the use of technical statistical jargon. Your reader will not necessarily understand complicated statistical terms, thus your task is to convert your analysis into plain, simple, easy to understand language.
- The email is to be written as a stand-alone document (assume that Boris Johns 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 in your email.
- Use an email format for your reply. That means the email heading (e.g. 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. Do not copy the questions in the email.
- Sequentially number your answers in both your email and your analysis (1, 2 ... 6) to match the questions asked in Boris Johns' email.
- Include a simple introduction at the start of the email and a summary/conclusion at the end.
- In your response email, marks will be deducted for the use of technical terms, the inclusion irrelevant material, poor presentation, poor organisation, poor formatting and emails that are over two pages long. Do not copy questions in the email.
When you have completed the email, 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 on re-reading, you become aware that you have made some points in a clumsy manner and find that you can re-phrase them much more clearly.
Part 3: Interactive Dashboard
The minimum requirement is a neat, functional, interactive dashboard. It is expected that the dashboard includes up to 5 interactive components. Your submitted Microsoft Excel file should contain a separate sheet for the interactive Dashboard.
The following questions will help guide you in designing an interactive dashboard.
1. What are the most appropriate visualisations for the dashboard?
2. What about colour choices?
3. How can I make the dashboard interactive?
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