应用数据分析代写 SEHS4605代写

Assignment Question Paper

应用数据分析代写 Plagiarism is a serious misconduct. No part of students’ assignment should be taken from other people’s work without giving them credit.

Subject Code	:	SEHS4605
Subject Title	:	Applied Data Analytics
Subject Lecturer’s Name		Dr. Wilson Leung 应用数据分析代写
Semester & Academic Year	:	Semester One, 2019/20
Submission Deadline to Moodle	:	14 December 2019 22:00 [PM]

Turnitin Submission is Required: [No]

This assignment question paper has a total of [13] pages (including this covering page).

Declaration of Original Work 应用数据分析代写

Plagiarism is a serious misconduct. No part of students’ assignment should be taken from other people’s work without giving them credit. All references must be clearly cited. Any plagiarism found in students’ completed assignments can lead to disciplinary actions such as mark deduction, disqualification or even expulsion by the School.

Prior to your submission, you MUST sign the “Declaration of Original Work” (template to be provided in Moodle) on the covering page of your completed assignment indicating that the work you have completed is your original work. The signed declaration must be submitted with your assignment.

Format

You MUST include (a) full student name, (b) student number, (c) subject code, (d) subject group (e) page number and (f) total number of pages on EVERY answer sheet.

Answer ALL questions in the answer sheet provided.

Question 1

(a) State THREE characteristics of information systems. (3 marks) 应用数据分析代写

(b) State TWO characteristics of open data and crowdsourced data respectively. State ONE example for each of them. (4 marks)

Question 2

Using the case of General Electric in Appendix A,

(a) state THREE characteristics of Big Data. (6 marks)

(b) state TWO challenges of conducting Big Data Analysis. (4 marks)

Question 3 应用数据分析代写

Answer the following questions using the SPSS output as shown in Appendix B.

(a) You are now asked to present the SPSS output based on the framework of a research model with relevant values which include the path coefficients, p-values, R² value, and their corresponding level of significance. (Hints: draw a diagram of the research model to present the figures above based on SPSS output in Appendix B)

(14 marks)

(b) Interpret R² value. Are you satisfied with the results of R² value? Why or why not?(4 marks)

Question 4 应用数据分析代写

Answer the following questions using the SPSS output as shown in Appendix C.

(a) Identify the dependent variable and describe what treatments (or procedure steps) that have been executed on the dependent variable before conducting the discriminant analysis.(3 marks)

(b) Identify all the significant independent variables in the Structure Matrix. Discuss the relationship between the discriminant loadings and the discriminant function.(5 marks) 应用数据分析代写

(d) Interpret the result findings based on the Structure Matrix and Functions at Group Centroids.(5 marks)

(e) Is the above result valid? (Why or Why not?). Justify your answer with the help of Table 1. (Hints: you should fill in the boxes in Table 1 based on the SPSS output in Appendix C.)

Firm size
Code
Size
Total number
Hand calculation

Table 1.(9 marks)

Question 5 应用数据分析代写

Answer the following questions using the SPSS output as shown in Appendix D.

A Two-way ANOVA test is used to compare the effects of three advertising methods (A, B, C) in five different advertising time periods (I, II, III, IV, V) on total sales

(a) Is there any significant interaction effect between advertising methods and advertising time periods? Justify your answer. (2 marks) 应用数据分析代写

(b) Are these three advertising methods (A, B, C) significantly different? Justify your answer.(2 marks)

(c) Are these five different advertising time periods (I, II, III, IV, V) significantly different? Justify your answer.(2 marks)

(d) State the significance of each group combination for these three advertising methods (A, B, C).(6 marks)

Question 6 应用数据分析代写

(a) Describe the concept of market basket analysis and state ONE example of market basket analysis for customer behavior.(2 marks)

(b) Describe TWO search marketing approaches. (4 marks)

Question 7

Figure 1.

(a) Name the web analysis in Figure 1. What are “A” and “B”?(3 marks)

(b) Describe the concept of the above web analysis. State TWO ways to set up this analysis.(6 marks) 应用数据分析代写

Question 8

(a) Describe the concept of KPI and its application in online business. (4 marks)

(b) Suggest THREE KPIs for website improvement and describe the equation of your proposed KPIs.(6 marks)

– END OF ASSIGNMENT-

Appendix A. 应用数据分析代写

Case study – General Electric

General Electric – a literal powerhouse of a corporation involved in virtually every area of industry, has been laying the foundations of what it grandly calls the Industrial Internet for some time now.

But what exactly is it? Here’s a basic overview of the ideas which they are hoping will transform industry, and how it’s all built around big data. If you’ve heard about the Internet of Things which I’ve written about previously a simple way to think of the industrial internet is as a subset of that, which includes all the data-gathering, communicating and analysis done in industry. In essence, the idea is that all the separate machines and tools which make an industry possible will be “smart” – connected, data-enabled and constantly reporting their status to each other in ways as creative as their engineers and data scientists can devise. 应用数据分析代写

This will increase efficiency by allowing every aspect of an industrial operation to be monitored and tweaked for optimal performance, and reduce down-time – machinery will break down less often if we know exactly the best time to replace a worn part.

Data is behind this transformation, specifically the new tools that technology is giving us to record and analyse every aspect of a machine’s operation. And GE is certainly not data poor – according to Wikipedia, its 2005 tax return extended across 24,000 pages when printed out.

And pioneering is deeply engrained in its corporate culture – being established by Thomas Edison,

as well as being the first private company in the world to own its own computer system, in the 1960s. So of all the industrial giants of the pre-online world, it isn’t surprising that they are blazing a trail into the brave new world of big data. GE generates power at its plants which is used to drive the manufacturing that goes on in its factories, and its financial divisions enable the multi-million transactions involved when they are bought and sold. With fingers in this many pies, it’s clearly in the position to generate, analyse and act on a great deal of data. 应用数据分析代写

Sensors embedded in their power turbines, jet engines and hospital scanners will collect the data – it’s estimated that one typical gas turbine will generate 500Gb of data every day. And if that data can be used to improve efficiency by just 1% across five of their key sectors

that they sell to, those sectors stand to make combined savings of $300 billion. With those kinds of savings within sight, it isn’t surprising that GE is investing heavily. In 2012 they announced $1 billion was being invested over four years in their state-of-the-art analytics centre in San Ramon, California, in order to attract pioneering data talent to lay the software foundations of the Industrial Internet.

In aviation, they are aiming to improve fuel economy, maintenance costs, reduction in delays and cancellations and optimize flight scheduling – while also improving safety.

Abu Dhabi-based Etihad Airways was the first to deploy their Taleris Intelligent Operations technology, developed in partnership with Accenture.

Huge amounts of data are recorded from every aircraft and every aspect of ground operations, which is reported in real-time and targeted specifically to recovering from disruption, and returning to regular schedule. 应用数据分析代写

And last year it launched its Hadoop based database system to allow its industrial customers to move its data to the cloud. It claims it has built the first infrastructure which is solid enough to

meet the demands of big industry, and works with its GE Predictivity service to allow real-time automated analysis. This means machines can order new parts for themselves and expensive downtime minimized – GE estimates that its contractors lose an average of $8 million per year due to unplanned downtime.

Green industries are benefitting too – its 22,000 wind turbines across the globe are rigged with sensors which stream constant data to the cloud, which operators can use to remotely fine-tune the pitch, speed, and direction the blades are facing, to capture as much of the energy from the wind as possible.

Each turbine will speak to others around it,

too – allowing automated responses such as adapting their behaviour to mimic more efficient neighbours, and pooling of resources (i.e wind speed monitors) if the device on one turbine should fail. Their data gathering extends into homes too – millions are fitted with their smart meters which record data on power consumption, which is analysed together with weather and even social media data to predict when power cuts or shortages will occur. GE has come further and faster into the world of big data than most 应用数据分析代写

of its old-school tech competitors. It’s clear they believe the financial incentive is there – chairman and CEO Jeff Immelt estimates that they could add $10 trillion to $15 trillion to the world’s economy over the next two decades. In industry, where everything including resources is finite, efficiency is of utmost importance – and GE are demonstrating with the Industrial Internet that they believe big data is the key to unlocking its potential.