Bus708 statistics and data analysis - present your data


1 OVERVIEW OF THE ASSIGNMENT

This assignment will test your skill to collect and analyse data to answer a specific business problem. It will also test your understanding and skill to use statistical methods to make inferences about business data and solve business problems, including constructing hypotheses, test them and interpret the findings.

Suppose you work in a company that provide services for people worldwide who are moving to Australia either permanently or temporarily (e.g. international students). One of the services it provides is to assist clients with choosing accommodation. Your task is to analyse the rent in different suburbs in Sydney Metro, specifically 4 different suburbs: Sydney, Randwick, Parramatta, and Auburn and analyse the rent that international students currently pay.

2 TASK DESCRIPTION: WRITTEN REPORT

Before you proceed, you need to have Dataset 1 and Dataset 2 ready:
Dataset 1: Collect data on international students' weekly rent (in Australian Dollars). There is no requirement about sampling methods and sample size, but you need to justify your approaches in Section 1 (see below).
Dataset 2: You will receive an email about the instruction to download the Rental Bond Board Property Data dataset that has been edited and allocated to you. This is a subset of NSW Rental Bonds data published by the Department of Finance, Services and Innovation. The original dataset can be obtained.

Both datasets should be saved in an Excel file (one file, separate worksheets). All data processing should be performed primarily in Excel, or by using Statkey tool.

1. Section 1: Introduction
a. Give a brief introduction about the assignment
b. Dataset 1: Explain how you collect the data and discuss whether or not your sample is biased. Is this primary or secondary data? What type of variable(s) is involved? You don't need to display your data in this section.
c. Dataset 2: Give a short description about this dataset. Is this primary or secondary data? What type of variable(s) is involved? Display the first 5 cases of your dataset.

2. Section 2: International Students' Weekly Rent
Use Dataset 1
a. Present your data using a suitable graphical display and numerical summary.
b. Make a short comment about the distribution of your data (e.g. the shape, centre, spread, outlier, and any interesting point).

3. Section 3: Rental Bond Board Property Data - Dwelling Type
Use Dataset 2
a. Examine just the data relating to Dwelling Type. Describe the data using a suitable graphical display and numerical summary.
b. Is there enough evidence that the proportion of House dwelling type is less than 10%? Perform a suitable hypothesis test at a 5% level of significance.
c. Describe the relationship between the variables Dwelling Type and Suburb using suitable graphical display and numerical summary.
d. What suggestions can you give for clients who would prefer to rent a house instead of a flat, in terms of their options?

4. Section 4: Rental Bond Board Property Data - Weekly Rent
Use Dataset 2
a. By considering residential with 2 bedrooms only, compare the weekly rent among the different suburbs using a suitable graphical display and numerical summary.
b. By considering residential with 2 bedrooms only, is there any evidence of the difference in weekly rent among the different suburbs? Perform a suitable hypothesis test at a 5% level of significance.
c. What suggestions can you give for clients who are deciding to rent in one of those suburbs, in terms of the Weekly Rent?

5. Section 5: Bond Amount
Use Dataset 2
a. Examine the relationship between the variables Weekly Rent and Bond Amount, using a suitable graphical display.
b. Calculate the correlation coefficient and make comments, including any outliers that may present and what suggestions can you give to clients regarding bond amount.

6. Section 6: Conclusion
a. What can you conclude about the weekly rent currently paid by international students and the weekly rent of properties on the market?
b. Give suggestion for future research

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