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1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 1 0 1 1 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 0 1 0.72354949 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0.4778157 0.19453925 0.09215017 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0887372 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0.2116041 UNSW School of Economics ECON1203 Business and Economic Statistics Session 1, 2016 Major project: Call of the Mall Purpose The idea is of this assignment is to simulate a professional work task involving statistical analysis. It is designed to help students learn about what is involved in undertaking basic statistical analysis to understand a realworld problem, and communicating the results of this analysis to business stakeholders. Context and Problem You are working as a business analyst for Your Market Consultants Australia (\"YMCA\"), a small Sydney-based company that specializes in providing statistical consulting services to Australian companies seeking intelligence about their present or potential customers. Your company is contacted by Eastfield, an owner of several large shopping centres on the Australian Eastern seaboard. You are told that the marketing director at one of Eastfield's malls, Betsy Lu, is interested in using market research to inform leasing decisions (i.e., decisions about which stores to sign as tenants of shop spaces in the mall). You attend a breakfast meeting with Ms Lu and a senior YMCA consultant to discuss her concerns in more detail. At the meeting, Ms Lu says: \"So the situation is that we hired a market-research firm to come in and run an intercept survey* for us, which they did, and the data were collected in a single week in October 2015. I'm now looking for someone with statistical expertise to analyse the data and give us some recommendations. The whole reason we did the survey is to find out what sort of shoppers we're attracting to the centre, so we have a better idea of what stores might do well in the mall. You see, the leases on about 30% of our tenants are up at the end of next year, and we want to know which kind of companies to approach in order to fill those vacancies. Some of our tenants have also struggled to pay their rent on time, which makes me wonder whether they're a good match to the centre. Just based on casual observation, my sense is that our biggest shopper segment is high-income young women, who stay at the mall for a long time and use their visits as social time with other young women, often with babies in tow. I'd expect that at least 20% of our customers are high-income young women, though I'm happy to be corrected on that. This would explain why our cafes and things like yogurt shops and Boost Juice type places are generally doing well. We also seem to have a lot of teenagers in the after-school time slot - I'd guess that they are our next-biggest shopper segment, though I'm not sure. We also have a community of elderly people in the neighborhood, some of whom are regulars. So, the first information I'm looking for is a breakdown of shopper types, both overall and by store type. I'd also like to know whether we're hitting Eastfield's internal minimum threshold of at least $60 spent on average per customer visit to the mall. Some information about the types of stores in which visits and spending are clustered for our different shopper types would also be of use. Anything you can tell me about how to improve our appeal to our main shopper types, of course, would be a great extra!\" Based on these comments, that afternoon you and the senior consultant draw up a consulting brief, which you send to Ms Lu, who approves it and hires you to perform the analysis required to address her concerns. Ms Lu then sends you an Excel file containing the data from the intercept survey. You are given the responsibility of providing a statistical analysis of these data and for producing an associated report that addresses Ms Lu's concerns. *intercept survey: a survey of shoppers leaving a shopping centre. Report It is natural to associate different parts of the project work with the coverage of relevant material in particular Sharpe chapters and lectures. However, remember you are writing a professional report, not providing a sequence of answers to assignment problems, so you should try to write a flowing report organized around ideas, not methods. The report should include a brief Executive Summary. Ms Lu, your audience, is an intelligent person but she is not a statistician. Your Executive Summary needs to be non-technical so that she can assess what you have done and understand your basic conclusions. The senior consultant at YMCA is familiar with technical analysis, and will be reviewing the report before it is sent out to the client to ensure that the conclusions discussed in the Executive Summary are reasonable and based on sound statistical analysis. You can see from the marking rubric below that while your project mark will be based primarily on the substance of your statistical work, the presentation of the material will also be considered. Reports should be typed and should use appropriate graphical techniques to represent the data, as well as other appropriate statistical analysis. For some guidance on what these types of reports might look like, please check the \"Project\" section of the course website where some actual examples of statistical reports are posted. As is the case in any actual work environment, there is no \"one right way\" to construct a report like this, and hence there is no rigid template for what your project should look like, nor is there only one way to approach the analysis. Data Each student will be assigned a personalized data set. Each individual data set refers to the problem above and has the same structure. However, values of some of the key variables will vary across students, meaning that statistical results and any inferences drawn from them may differ across students. In the \"Project\" folder on the website, you will find an Excel file (\"Project Data.xls\") that you will download in order to obtain your personalized data set. Once you enter your zID into the highlighted cell in the Excel worksheet, a personalized data set will be generated using which you should perform your analysis. Copy and paste the generated data into a new Excel workbook, where you will conduct your analysis. Each data set will contain a sample of 293 observations, where each observation refers to a separate survey response. There are 13 variables, as follows: pid = survey response identifier visits = total number of stores visited on this visit dayofweek = day identifier, taking values 1 (Monday) through 7 (Sunday) freqmallvisit = responder's estimate of his/her number of visits to this mall in the past three months 2 spend = responder's estimated amount spent in mall stores, in total, on this visit (AUD$) age = binary indicator of respondent's age (assessed by interviewer), where 0=under 15 1=15-20 2 = 21-30 3 = 31-40 4 = 41-50 5 = 51-60 6 = 61 or over HHinc = total household income of the respondent, where: 1=less than $40,000 per year; 2=$40,000-$80,000 per year; 3=$80,000-$120,000 per year; 4=$120,000-$160,000 per year; 5=$160,000-$200,000 per year; 6=$200,000 or more per year. sex = binary indicator of respondent's sex (assessed by interviewer), where 1=female 0=male food = binary indicator where: 1=the respondent made a purchase at one or more specialty food/beverage stores on this visit 0=the respondent did not make a purchase at a specialty food/beverage store on this visit apparel = binary indicator where: 1=the respondent made a purchase at one or more specialty apparel stores on this visit 0=the respondent did not make a purchase at a specialty apparel store on this visit department= binary indicator where: 1=the respondent made a purchase at one or more department stores on this visit 0=the respondent did not make a purchase at a department store on this visit grocery= binary indicator where: 1=the respondent made a purchase at one or more grocery stores on this visit 0=the respondent did not make a purchase at a grocery store on this visit other= binary indicator where: 1=the respondent made a purchase at one or more other types of stores on this visit 0=the respondent did not make a purchase at another type of store on this visit Tutorial work You should think about the problem and plan what needs to be done before you actually start undertaking extensive computing work. In order to assist in this process, there will be some project discussion time in tutorials. In particular, in your Week 7 workshop you will be asked to review the problem and to discuss with other students what is required by way of analysis. You should generate your data, and then copy, paste, and save it into a new Excel workbook that you bring along to class as part of your preparation for your Week 7 workshop. In this Excel workbook, you should take time before class to generate some descriptive statistics for your data that you can show your tutor in Week 7. This will confirm that you have correctly created your data set and that you are ready to commence your analysis. These descriptive statistics will also be required to answer a Week 7 workshop question. 3 In your Week 9 workshop, time will be devoted to monitoring your progress and discussing how more recently covered material might be relevant to the project tasks. Naturally you should consult your tutor or lecturer should you have any other questions regarding the project. Details Length: Cover sheet: Project mark: Due date: Late submission: Plagiarism: Coverage: No longer than 1200 words, excluding tables and graphs. Total length including all discussion, tables and graphs should not exceed 8 pages. Attach the project assessment sheet (given below) as your first page. Check that you have completed the top (grey) sections in full, including your signature. Do not use plastic sheets or binders. Simply attach the completed cover sheet and staple the pages together. The project will be marked out of 20, and will constitute 20% of your total course mark. A hard copy of your assignment is to be submitted to your tutor in or before your scheduled workshop in week 11 (beginning May 16). In addition to the hard copy, you must also submit an electronic copy to the course website by 6 pm on Friday May 20. Upload your assignment via the Turnitin link on the course website. Browse and upload a copy of your document - do not paste text. Use your student zID in the file name (e.g., z1234567.doc). 20% of the value of the project will be deducted for every day or part thereof that the hard copy is turned in after the start time of your Week 11 workshop (including the weekend). Projects submitted more than five days late will not be marked and will be assigned a mark of zero. You can and should be working on the material regularly before the due date. Extensions will only be granted in exceptional circumstances and must be approved by the lecturer-in-charge. For more details about late submission, see the Course Outline. While you are encouraged to discuss the project with other students, each person must submit an individual assignment that represents his or her own work. All assignments will be checked for plagiarism on the Turnitin software into which they are uploaded. The software will automatically check against all other projects ever submitted. Material copied from previous projects will be found and the source will be identified. Evidence of plagiarism will be treated extremely seriously: automatic and immediate failure in the course is a possible penalty. See Part B of the course outline for further details about UNSW's policies on plagiarism. If you are in doubt about how to identify or avoid plagiarism, follow the link to and complete the self-paced Working with Academic Integrity module on Moodle. In your work for this project, you are only expected to use the statistical techniques developed in the text and lectures up until the end of week 9. 4 ECON1203 Business and Economic Statistics PROJECT ASSESSMENT Name ___________________________________________ zID __________________ Workshop Group (Tutor, Time and Place) ________________________________________ I declare that this assessment item is my own work except where acknowledged, and acknowledge that the assessor of this item may, for the purpose of assessing this item: (1) Reproduce this assessment item and provide a copy to another member of the University; and/or (2) Communicate a copy of this assessment item to a plagiarism checking service (which may then retain a copy of the assessment item on its database for the purpose of future plagiarism checking) I certify that I have read and understood the University Rules in respect of Student Academic Misconduct. Signed ______________________________________ Date _____________________ ANALYSIS and DISCUSSION (14 marks) Characterizing key features of the data (7 marks) Missing ( ) Poor ( ) Adequate ( ) Good ( ) Excellent ( ) Motivation, production and evaluation of hypotheses (3 marks) Missing ( ) Poor ( ) Adequate ( ) Good ( ) Excellent ( ) Justification of main conclusions (2 marks) Missing ( ) Poor ( ) Adequate ( ) Good ( ) Excellent ( ) Executive summary (2 marks) Missing ( ) Poor ( ) Adequate ( ) Good ( ) Excellent ( ) MECHANICS (6 marks) Presentation (3 marks) Poor ( ) Adequate ( ) Good ( ) Excellent ( ) Use of graphics (3 marks) Missing ( ) Poor ( ) Adequate ( ) Good ( ) Excellent ( ) Other Some problems with spelling ( ) Some problems with grammar ( ) Poor spelling ( ) Poor grammar ( ) Some confused or off-target discussion and interpretations ( ) Well argued ( ) Too long (you need to condense your argument) ( ) GENERAL COMMENTS 5