Case Study
You are Lee Slattery, an analyst for BI Intelligence. BI Intelligence is the Business Insider’s paid research service. Business Insider is the world’s fastest-growing business news website, and its articles include the latest technology, money and market news. BI Intelligence produces a number of reports on key digital areas, including the mobile industry. Information from those reports is then subsequently published on the Business Insider’s website by their respective writers.
Sam Edmondson, a tech journalist at Business Insider, wants to publish an article on the current smart mobile phone usage in Australia. This is in light of Australia being ranked 2nd in the world behind Singapore for smartphone usage. Businesses, including Telcos, find this information useful and subsequently use it to improve their own operations, marketing strategies, etc., for the digital age. Sam’s article will be wide-ranging and include commentary on the user’s expenditure, usage patterns, satisfaction levels and demographics.
Sam has asked you to conduct a preliminary analysis of the collected data. In particular, you are expected to perform a series of descriptive and inferential analyses and produce a report based on the findings. This report must be written in plain language since the interested party who may read the report do not necessarily have any statistical knowledge.
Sam’s specific analysis requirements are outlined in his email, which is reproduced on the next page.
Assignment-1 Descriptive Analytics and Visualisations Page 1 of 6 MIS771 Descriptive Analytics and Visualisation Assignment One Background This is an individual assignment. You need to analyse a given data set, and then interpret and draw conclusions from your analysis. You then need to convey your conclusions using plain language in a written report to a person with little or no knowledge of Business Analytics. Percentage of final grade 30% The Due Date and Time 11.59 PM Sunday 7th April 2019 Submission instructions The assignment must be submitted by the due date, electronically in CloudDeakin. When submitting electronically, you must check that you have submitted the work correctly by following the instructions provided in CloudDeakin. Please note that we will NOT accept any paper or email copies, or part of the assignment submitted after the deadline. No extensions will be considered unless a written request is submitted and negotiated with the unit chair before Thursday 4th April 2019, 5:00 PM. Please note that assignment extensions will only be considered if you attach your draft assignment with your request for an extension. You must keep a backup copy of every assignment you submit (that is, the work you have done to date) until the assignment has been marked. In the unlikely event that an assignment is misplaced, you will need to submit your backup copy. Work you submit will be checked by electronic or other means to detect collusion and/or plagiarism. When you submit an assignment through your CloudDeakin unit site, you will receive an email to your Deakin email address confirming that the assignment has been submitted. You should check that you can see your assignment in the Submissions view of the Assignment Dropbox folder after upload, and check for, and keep, the email receipt for the submission. Penalties for late submission: The following marking penalties will apply if you submit an assessment task after the due date without an approved extension: 5% will be deducted from available marks for each day up to five days, and work that is submitted more than five days after the due date will not be marked. You will receive 0% for the task. 'Day' means calendar days or part thereof. The Unit Chair may refuse to accept a late submission where it is unreasonable or impracticable to assess the task after the due date. The assignment uses the file A1.xlsx, which can be downloaded from CloudDeakin. Analysis of the data requires the use of techniques studied in Module-1. Descriptive Analytics and Visualisations Page 2 of 6 Assurance of Learning This assignment assesses the following Graduate Learning Outcomes and related Unit Learning Outcomes: Graduate Learning Outcome (GLO) Unit Learning Outcome (ULO) GLO1: Discipline-specific knowledge and capabilities - appropriate to the level of study related to a discipline or profession. GLO3: Digital Literacy - Using technologies to find, use and disseminate information GLO5: Problem Solving - creating solutions to authentic (real-world and ill-defined) problems. ULO 1: Apply quantitative reasoning skills to solve complex problems. ULO 2: Use contemporary data analysis and visualisation tools and recognise the limitation of such tools. Feedback before submission You can seek assistance from the teaching staff to ascertain whether the assignment conforms to submission guidelines. Feedback after submission An overall mark together with suggested solutions will be released via CloudDeakin, usually within 15 working days. You are expected to refer and compare your answers to the suggested solutions to understand any areas of improvement. Descriptive Analytics and Visualisations Page 3 of 6 Case Study You are Lee Slattery, an analyst for BI Intelligence. BI Intelligence is the Business Insider’s paid research service. Business Insider is the world’s fastest-growing business news website, and its articles include the latest technology, money and market news. BI Intelligence produces a number of reports on key digital areas, including the mobile industry. Information from those reports is then subsequently published on the Business Insider’s website by their respective writers. Sam Edmondson, a tech journalist at Business Insider, wants to publish an article on the current smart mobile phone usage in Australia. This is in light of Australia being ranked 2nd in the world behind Singapore for smartphone usage. Businesses, including Telcos, find this information useful and subsequently use it to improve their own operations, marketing strategies, etc., for the digital age. Sam’s article will be wide-ranging and include commentary on the user’s expenditure, usage patterns, satisfaction levels and demographics. Sam has asked you to conduct a preliminary analysis of the collected data. In particular, you are expected to perform a series of descriptive and inferential analyses and produce a report based on the findings. This report must be written in plain language since the interested party who may read the report do not necessarily have any statistical knowledge. Sam’s specific analysis requirements are outlined in his email, which is reproduced on the next page. Descriptive Analytics and Visualisations Page 4 of 6 Email from Sam Edmondson To: Lee Slattery From: Sam Edmondson Subject: Analysis of Smartphone data Hi Lee, As discussed earlier, I have cleaned and simplified the dataset to 11 variables for your convenience. The cleaned dataset contains information about 150 randomly selected smartphone users. 1. Please provide an overall summary of the monthly bill amount. 2. I would like to build a profile of a typical smartphone user. For a start, please estimate: a. the average percentage use of Smartphones for work-related activities. b. the proportion of Smartphone users classified as geo tribe ‘Crusaders’. Please include any other factors that you might think would be appropriate to include in the profile of the Smartphone user. 3. I would also like to compare our data against several industry publications. a. An industry report suggests that the average monthly bill has dropped below $72. Is there any evidence to support this argument? b. A similar study last year reported that 3 out of 4 Smartphone users are either ‘Very Satisfied’ or ‘Moderately Satisfied’ with their service provider. Would you please check to see if this statement is still valid for all users? c. If we delve deeper, is there a difference in the level of satisfaction between male and female users? In other words, can we conclude that there is a significant difference between how males and females users feel about their service provider? d. As an industry benchmark, it is believed that the monthly bill amounts are related to the use of the Smartphone as a payment device. In particular, the average monthly bill is higher when using the smartphone as a payment device. Does the data support this proposition? e. Also, the industry experts believe that the use of Smartphones for online purchases is higher for users who are highly active in social media than for those users who are moderately active in social media. Is this a valid statement? 4. I would like to get an understanding of the link between the level of social media engagement, gender and the average monthly bill amount. a. I believe that the proportion of female users who are moderately active in social media is higher than that of male users who are moderately active in social media. b. I also believe that the average monthly bill amount for users who are moderately active in social media is higher when the Smartphone user is female. Is there any evidence to support my assertions above? I look forward to your response. Sincerely, Sam Edmondson Descriptive Analytics and Visualisations Page 5 of 6 SUBMISSION The assignment consists of two parts: Analysis and Report. You are required to submit both your written report and analysis. Guidelines for Data Analysis Read the case study and questions asked by Sam carefully. Then spend some time reviewing the data to get a sense of the context. The analysis required for this assignment involves material covered in Module 1, with the corresponding tutorials being a useful guide. The analysis should be submitted in the appropriate worksheets in the Excel file. Each question from the email should be analysed in a separate tab (e.g. Q1, Q2 … or Q3.1, Q3.2 …). You need to add these. Before submitting your analysis, make sure it is logically organised, and any incorrect or unnecessary output has been removed. Marks will be penalised for poor presentation or disorganised/incorrect results. For all questions in the email, you can assume that: • 95 % confidence level is appropriate for confidence intervals and; • 5.0 % level of significance (i.e. α = 0.05) is appropriate for any hypothesis tests. You can complete all data analysis using the Excel templates provided in the assignment data file. In choosing the technique to use for a given question, keep the following in mind: • Are we dealing with a numerical (quantitative) variable or categorical (qualitative) variable? • Do we have to make an estimate or are we testing a theory, claim etc.? Each type of question must be answered using the most appropriate technique. • Are we dealing with one sample/population? • Are we dealing with two samples/populations (independent samples or pair-samples)? • Even though question(s) may lead you to inferential analysis, consider conducting a descriptive analysis of the sample data first. ATTENTION! When you have established that there is a difference between two means or proportions, we expect you to estimate and report the difference. You may need to make certain assumptions about the dataset we are using to answer some questions. For other questions, there will be technical/statistical assumptions that you need to make; for example, whether to use an equal or an unequal variance test. Note: Give the Excel file the following name A1_YourStudentID.xlsx (use a short file name while you are doing the analysis. Descriptive Analytics and Visualisations Page 6 of 6 Guidelines for your Business Report Once you have completed your data analysis, you need to summarise the key findings for each question