Descriptive Analytics and Visualisations Page 1 of 9 MIS771 Descriptive Analytics and Visualisation Assignment One Background This is an individual assignment, which requires you to, first, analyse a given data set, 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 Monday 7th August 2017 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 Dr Kia Kashi before Thursday 3rd August 2017, 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.Descriptive Analytics and Visualisations Page 2 of 9 The assignment uses the file FastFoodData.xlsx, which can be downloaded from CloudDeakin. Analysis of the data requires the use of techniques studied in Module-1. 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 9 Background to the Project Review the following article* (Source: news.com.au, accessed on 23 June 2017): * http://www.news.com.au/finance/work/at-work/sunday-penalty-rates-slashed-by-fair-work-commission/newsstory/debbd0c1fd8de8fe3898ca51950b4f5bDescriptive Analytics and Visualisations Page 4 of 9 CASE DESCRIPTION After a recent announcement by Fair Work Commission (FWC) about their decision to cut “Sunday and Public holiday penalty rates”, union groups started to express concern about possible effects of such rate cuts on Full-time and Part-time hospitality workers (particularly, in the fast-food industry). The unions argue that such rate cuts directly affect fast-food workers earnings. However, advocates of the new policy suggest that a) pay cuts allow fast-food businesses to employ more workers (both part-time and full-time), and b) such pay cuts help them become more competitive by enabling them to cut down food prices – which in turn benefit end-customers. Consequently, the FWC decided to formally investigate possible economic effects of “Sunday and Public holiday penalty rate cuts” at both micro (i.e., change in product prices and business day-to-day operations) and macro levels (i.e., Full-time and Part-time employment). This helps the FWC to have a better picture of possible outcomes of implementation of the policy. Accordingly, the FWC has put a research project ‘out to tender’ and invited Victorian Universities to make proposals for the project. Deakin Business School – and specifically the Department of Information Systems and Business Analytics (DISBA) – is planning to bid for this research project and respond to the FWC’s ‘request for tender (RFT)’. To successfully bid for government tenders, DISBA must demonstrate its domain expertise as well as analytical skills in conducting these types of projects. DISBA has now put a research team together to prepare a proposal. To strengthen the proposal, the research team is planning to use historical data – in particular US-based research conducted in 2005 in a similar context to the FWC’s project – and conduct analysis in order to demonstrate their research, data analysis, and reporting capabilities. BACKGROUND TO THE US STUDY The US Fast Food Data was collected for the purpose of studying the effect of a decrease in the penalty rates on employment. According to conventional economic theory, perfectly competitive employers will always cut their work force in response to any rise in the penalty rates, or vice versa (i.e. increasing workforce in response to a penalty rate cut). In practice, however, employer reactions are not so clearcut: While some studies in the seventies confirmed the predictions of theory, earlier studies from the sixties, as well as more recent studies conducted in the early twenty century, concluded that employment was unaffected by change in the penalty rates. The US study sought to clarify the issue in the early twenty century. The New Jersey (NJ) Sunday and holidays penalty rate decrease of April 1, 2005, which cut the minimum wage from $8.15 to $7.15 per hour (12 percent cut), provided a perfect opportunity. To evaluate the impact of the law, the NJ Minimum Wage Advisory Commission surveyed 410 Fast Food restaurants in New Jersey and eastern Pennsylvania before and after the cut. Details of the US research is presented in the following section. The US research is very similar to what DISBA team may end up doing for the FWC and therefore, a re-examination of US data will clearly demonstrate DISBA ability to conduct the FWC’s proposed research.Descriptive Analytics and Visualisations Page 5 of 9 US DATA (FastFoodData.xlsx file) The study chose to assess the effects of the penalty rate cuts on a random sample of Burger King, Wendy’s, KFC, and Roy Rogers restaurants in New Jersey and eastern Pennsylvania because the fastfood industry employs predominantly low-wage workers; the absence of tips simplifies the measurement of wages; and because fast-food restaurants are relatively easy to sample. The restaurants were interviewed about one month before, and about eight months after, the penalty rate cuts went into effect. Information was collected at each restaurant about variables such as the number of employees, various product prices, and store hours. The data provided enabled the reviewers to calculate the impact of the penalty rate cuts on not only on employment rates, but also on food prices, which can always be cut in order to stay more competitive. The final data set contains information on 410 restaurants randomly chosen from phonebooks in New Jersey and eastern Pennsylvania. For each restaurant, information is provided on 40 variables; about half pertain to the period before the minimum wage increase (colour coded in BROWN in the excel file), and about half concern the period after (colour coded in BLUE in the excel file). After it was chosen from the phonebook, each restaurant was called for a telephone survey. To elicit a response restaurants were called back as many as nine times, and the researchers obtained 387 completed interviews – a 94 percent response rate. More details on the variables included in the study can be found in the Excel file, under Variable Description tab. YOUR ROLE AS A RESEARCH ASSISTANT You are a part-time research assistant doing sessional work at DISBA. The research team leader – Edmond Kendrick – has asked you to conduct preliminary data analysis on the US-data. In particular, you are expected to perform a series of descriptive and inferential analyses on the US-data and produce a report based on the findings. Report must be written in plain language since the interested party who may read the report do not necessarily have statistical knowledge. Edmond has sent you an email detailing the questions you need to answer through analysing the USdata. His email is reproduced below:Descriptive Analytics and Visualisations Page 6 of 9 Email from Edmond Kendrick To: YOU From: Edmond Kendrick Subject: Analysis of US Fast Food Data – FWC Proposal Greetings, Welcome aboard and thank you for agreeing to conduct the preliminary analysis on the US data. Here are the questions we wish you to answer by undertaking appropriate descriptive/inferential analysis: 1. One of the first questions we need to answer from the US-data is the extent of the impact the new policy has had on employees in US Fast Food industry. a. Could you please estimate the average percentage of all staff that were affected by the penalty rate cut? b. I would assume that the extent of the impact was similar across both states of New Jersey and Pennsylvania. Could you examine the data and see if the average percentage of all staff affected by the new policy was different across the two states? c. Since both KFC and Burger Kings have operations here in Australia*, it would be interesting to see how these two Fast Food giants reacted to penalty rate cuts in US back in 2005. So your job is twofold: first, examine if there is a difference in the percentage of all staff who were affected by the new policy across these two companies; and if there was a noticeable difference, please estimate the level of that difference? 2. It is common for Fast Food companies to compensate new workers in a form of a ‘cash bounty’. So, let’s have a look at the bonus policy across US Fast Food industry. a. A 2004 independent study by the US Wage Advisory Commission (WAC) showed that 30 percent of all Fast Food companies compensated their new workers in the form of a cash bounty. Is there any evidence to support WAC’s finding? b. Back in 2004, the WAC reported their findings with 99 percent confidence intervals and one percent (1%) significance level. Re-do your analysis based on these values and see if a change in confidence/significance level would affect your own results and thus your conclusions regarding WAC’s findings. c. Compared to Wendy’s and Roy Rogers, KFC and Burger King are considered as dominant competitors in the US Fast Food industry and thus, the two can exert stricter compensation policies on new employees. This made me think that the proportion of all KFC and Burger Kings stores combined that offer cash bounty to the new employees may be less than that of all Wendy’s and Roy Rogers combined. Please test this assumption and get back to me with your findings. 3. Let’s focus on overall employment status across US Fast Food industry prior to the rate cut. a. Can you estimate the average number of full-time employees working in this sector? b. Please do the same calculation for part-time employees as well. c. Again, let’s focus on KFC and Burger Kings. Find out if the true average number of part time employees at Burger King is greater than that of KFC. If there was a noticeable difference, please estimate the extent of that difference. * Hungry Jack's Pty Ltd is the exclusive Australian master fast food franchise of Burger King Corporation.Descriptive Analytics and Visualisations Page 7 of 9 Now, let’s move on to examining the effect of the ‘penalty rate cut policy’ had on operations and employment in US Fast Food industry. 4. Unions believe that any penalty rate cuts will negatively affect employees’ earnings. Advocates of such policies on the other hand believe that these policies allow employers to reimburse employees in other forms such as an early pay rise, or the amount of first pay rise. Could you examine the 2005 US-data and tell me whether: a. The average amount of time staff had to work for the Fast Food before becoming eligible for a pay rise had significantly has reduced after implementing the ‘penalty rate cut’ policy? If supported, could you please estimate the difference? b. What about the average amount of first pay rise? I would assume there was no change in the average amount of first pay rise as a result of implementing the policy. Can you test my assumption and report back to me? 5. On the other hand, advocates of the penalty rate cut policy claim that implementing such a policy allows them to cut food prices, improve service and thus, stay competitive. Let’s see if we can provide support by examining the following: i. The average price of a meal* has reduced after implementing the policy. ii. The average number of hours Fast Food stores are open has increased after implementing the policy. This assumption will help us understand whether the Fast Food day-to-day operations have been affected by the penalty rate cuts. a. Can you test these two assumptions and, ONLY if you managed to confirm any, tell me how much the difference has been in the scenarios mentioned above? 6. Once you finalised your analyses and reported your findings, reflect on the overall issue under investigation. Given your findings on the possible effects of penalty rate cut policy on employment, food prices, and Fast Food operations, would you recommend implementing the “Sunday and Public holiday penalty rate cuts” here in Australia? Explain. 7. Finally, if we won the project and decided to replicate the same study here in Australia, what sampling method is most suitable to ensure representativeness of the sample? Why? Are there any possible limitations with your chosen sampling method? Explain. I look forward to your response. Sincerely, Edmond Kendrick PhD in Business Analytics, Department of Information Systems and Business Analytics Deakin Business School | Deakin University 70 Elgar Road, Deakin University, Melbourne Burwood Campus, VIC 3125, Australia +61 (3) 9244 3927 Deakin University CRICOS Provider Code 00113B * Meal is a combination of an entrée, a small fries and medium soda.Descriptive Analytics and Visualisations Page 8 of 9 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 Edmond 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 Edmond’s Email should be analysed in a separate tab (e.g. Q1, Q2 … or Q3.1, Q3.2 …). If you need more worksheets, then add them. 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 successfully 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 a proper 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 descriptive analysis of the sample data first. ATTENTION! Although sometimes either a confidence interval or a hypothesis test could be used to answer the same question, you MUST use ONLY the best technique (either CI or H-testing) to answer the question. 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 9 of 9 Guidelines for your Business Report Once you have completed your data analysis, you need to summarise the key findings for each question and write a response to Edmond’s questions in a report format. Your business report consists of four sections: Introduction, Main Body, Conclusion, and Appendices. The report should be 2,500 (± 300) words. Use proper headings (e.g. Q1, Q2 … or Q3.1, Q3.2…) and titles in the main body of the report. Use subheadings where necessary. Keep the language plain and the explanations succinct. That is, avoid the use of any technical statistical jargon. Your reader may not necessarily understand even simple statistical terms, thus your task is to convert your analysis into plain, understandable expressions. General instructions: • You MUST report both descriptive and inferential analysis results. Otherwise, marks will be deducted. • The report is to be written as a stand-alone document (assume Edmond will only read your written report). Thus, you should not have any direct references in the report to your analysis. • Your report may include relevant excel outputs including templates, tables, charts, and graphs but ONLY as Appendices (appendices are not included in the word count). • Make sure these outputs are visually appealing; have a consistent formatting style and proper titles (title, axes titles etc.); and are numbered correctly. Where necessary, refer to these outputs in the main body of the report. • Introduction begins by highlighting the main purpose(s) of analysis and concludes by explaining the structure of the report (i.e., subsequent sections). Conclusion should highlight the key findings of analyses and explain the main limitations (if any). • Marks will be deducted for the use of technical terms, irrelevant material, and poor presentation/organisation. When you have completed the report, it is a useful exercise to leave it for a day, return to it and then re-read. Does it flow easily? Does it make sense? Can someone without prior knowledge follow your written conclusions? Often, on re-reading, you become aware that you have made some points in a clumsy manner, and you find that you can re-phrase them much more clearly. Note: Give the report the following name A1_YourStudentID.docx.