BUS5PB: Principles of Business Analytics- Real-World Analytics Setting- Report Writing Assignment

Internal Code: 1HFHE

Report Writing Assignment:

Task: The second assignment focuses on the application of descriptive (and inferential) analytics techniques in a real-world analytics setting. It is intentionally ‘open-ended’ so that you develop a strong understanding of the business value of descriptive analytics. Domainsights, a recently formed real estate buyer’s advocacy firm is looking to enter the Melbourne property market. The senior management are keen to capitalise on large volumes of historical real estate data to generate insights on various aspects of this booming market. Insights can range across location, seasonality, price trends, area features, land features, property features and various other aspects. The firm has acquired a large dataset of real estate sales in Melbourne, close to 20,000 records from 2017. You have been hired as an expert descriptive analyst to demonstrate the application of descriptive and inferential analytics techniques using R programming, in the context of real estate buyer advocacy. What you are required to do:

• Conceptualise a business scenario (use case) to demonstrate the application of descriptive analytics techniques. The scenario can be based on an external reading, your own observations and experiences or simply a full sweep of the whole market. It should ideally consist of several business questions. Select questions that would demonstrate the variety of techniques practiced in class.
• Compile structured hypothesis tests that reflect these business questions. Account for all elements of a hypothesis test.
• Select appropriate samples (or sample) from the given dataset, with justification.
• Apply the correct descriptive and/or inferential analytics technique to address the business question. Techniques must be applied using R programming, no other statistics software is permitted.
• For each business question, provide a business interpretation of the generated statistic. You may use graphs and charts to support your explanation.
• Assume a normal distribution as per the central limit theorem, unless a technique explicitly requires a specific distribution.

The dataset contains some missing/erroneous values, you should account for these discrepancies.