Economics Homework Help

Word Limit and Submission requirements:、

• 2500 word limit (excluding visualisations). Visualisations are counted in the overall effort. The overall effort counts towards a total of 3000 words.

• Submission document requirements:

o Include question numbers with the answers (1.a., 1.b.,…,2.a.i,…, 2.b.i,2.b.ii,…)

o Questions that you do not answer will receive zero marks.

o Format: Double line spacing, 2.5cm margins, and font size of no less than 12pt. o Student Identification (SID) must appear in the header on the top right-hand side of each page of the document. Do not include your name. o Page number should be bottom right-hand side of each page.

o File name should be of the format: SID-StreamNumber-Year-Semester § Example: 1234567-Stream2-2021-S1.docx

o Assignments should be print-friendly and should not contain a cover sheet.

o Assignment can be submitted as Microsoft Word document or as pdf.

o Word Length: Where the student exceeds the word length, the student will lose 10% of the total marks when the submission is 10% above the word length and 10% for each 10% over-length thereafter.

• Submit an electronic copy via Canvas by the due date.

o Use the submission link provided on Canvas in the Assignments Tab (located on the left-hand menu bar).

o Should submission problems occur, please contact the unit administrator as soon as possible.

• Submit Tableau visualisation via Tableau Online (see ‘Notes’ section for more details).

Avoid using foreign languages in the assignment, submission documents, and visualisations.

• References

o All references are included in the overall word count.

o Make sure appropriate referencing and referencing style is used (e.g., APA). See Canvas for more information.

This assignment has three parts: • Part 1L Essays for parts: 1.a, 1.b, 1.c, and 1.d (1000 words) • Part 2: Essays for parts: 2.a.i, 2.a.ii, 2.a.iii, 2.a.iv, 2.b.i, 2.b.ii, 2.b.iii, and 2.b.iv (1500 words) • Part 2.b: Visualisation (Tableau Dashboard that fits in 1 page or on one screen in standard resolution)

Part 1: Read the case of the: Personal location data (Veale, M. “Data management and use: Case studies of technologies and governance.”, 2018; pdf posted on canvas). Additionally, conduct independent research on the topic of ‘personal location data’ and find an “interactive” visualisation corresponding to any one of the five modes of self-tracking (as per section 6.1 of the case study). Then, answer the follow questions related to the visualisation:

a. Provide the Internet link (url) to the visualisation. Explain which mode of self-tracking the visualisation applies to? how did you come to this conclusion?

b. Use data visualisation frameworks and decision contexts discussed in weeks 1 and 2 to analyse the visualisation. Explain the decision context, the purpose, function, and form of the visualisation.

c. What are the key takeaways from the visualisation?

d. Critically reflect on the visualisation based on week 4 topic: ‘Designing Effective Visualisations’. Provide at least three examples of good practices used in the visualisation. Provide three examples of areas for improvements. Explain your rational in detail.

Part 2: Read the 3DFit Studio case study (see Appendix at the end of this document). Then, answer the following: a. Business Understanding: Conduct independent research on the topic to understand the business model of 3D scan-based fitness management. Provide justifications based on your research.

i. Describe your understanding of the business model of 3D scan-based fitness management globally?

ii. What assumptions do you explicitly make about the underlying business model of 3D scan-based fitness management?

iii. What type of customers are most likely to use 3D scan for fitness management? How popular is 3D scan-based fitness management?

iv. What motivates customers to use 3D scan for fitness management?

b. Data Understanding of the 3D scan dataset:

o Use Tableau data visualisation to conduct exploratory data analysis (EDA) as discussed in week 5. Specifically focus on the features that you believe are most relevant to the business model of 3D scan-based fitness management. Conduct the following EDA analysis: • Distribution characteristics • Measures of central tendency (mean, median, mode) • Variance and standard deviation

o Use Tableau data visualisation to explore correlation among features (see footnote for examples1,2 ):