ECON1203 Business and Economic Statistics

Session 2, 2015

Major project: BreezyRealty

Project Data

To obtain your personalized data, go to the Excel file ‘Project_Data’ and enter you student ID number (without the `z’). You NEED to copy the dataset and save to another Excel worksheet for analysis. Please ensure that you enter your ID correctly. If you get a message ‘no such person’, that means that your ID number was not in the latest students list I have. Just email me (rouysse@unsw.edu.au) and I will update the list. Check that the top row is all zeros!

Purpose

The idea is of this assessment 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 real world problem, and communicating the results of this analysis to business stakeholders.

Outline

You are working as a business analyst for Crunch-IT Consultants, a Sydney-based company that specializes in providing statistical consulting services to small and medium-sized businesses. Your company is contacted by BreezyReality, a real estate agency who specializes in selling properties in the eastern suburbs. You attend a lunch meeting with a senior Crunch-IT consultant and BreezyRealty owner-manager, Stella Jarcque-Bera, to discuss Stella’s concerns. At the meeting, Stella says:

“First, I’d like to know whether any of the four sales agents I’ve hired are underperforming relative to their peers. Sometimes it seems like there are big differences across my employees in how long it takes to sell a property, and in how satisfied property owners are with the service provided – but maybe I’m imagining this.

Of course, as with any business, ensuring I’ve got satisfied customers is key to generating repeat business and word-of-mouth referrals, and I’d like to see at least 80% of my customers satisfied with the experience they get in selling their property with BreezyRealty.

Second, with the current hot property market, we’re getting more and more listings and owners wanting to cash on the rising housing prices. Increasingly, more and more listings are from areas further away from the beach front and there is quite a variation in the age of the properties listed and sold. I worry that some properties might be less competitive on the market and take longer to sell because they are further away from the beach and/or because of the number of years since they were built. This means that our pricing structure for property listing should change. You see, at the moment we charge a set price for each type of property, regardless of its distance from the beach. In addition, old and new houses are charged the same. I want to know if we should be charging according to the distance to the beach and/or age of the property.”

Based on these comments, later that afternoon you and the senior consultant draw up a consulting brief, which you send to Stella, who approves it and hires you to perform the analysis required to address her concerns. In the brief you describe the type of data you’ll need, and the next week, Stella sends you an Excel file containing historical data on property sales performed over the past year that has been pulled from BreezyRealty’s computer system.

When a home owner comes into BreezyRealty to list his/her property, a front-office clerk assigns one of the agency’s four sales agents to the job. Each sales agent keeps a time sheet on which he/she records the number of days until the property is sold, and also records some specifics about the property, including an estimate of its age and its distance from the nearest beach. When the property is sold the customer is asked to rate his/her satisfaction with the service/sale experience. Stella mentions that not all the property listings performed by BreezyRealty in the last year are recoded in these data. Sometimes the data are not entered into the system when a sale is completed and some properties are ‘under offer’ and are not recorded as final sales.

You are given the responsibility of providing a statistical analysis of these data and for producing an associated report that addresses Stella’s concerns.

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. Stella, 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 Crunch-IT 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.

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 a link to obtain your personalized data set. Click on the link

and you will be taken to a screen where you will be asked to enter your student ID number. A personalized

data set will then be produced in an Excel file. Each data file will contain a sample of 293 observations, where

each observation refers to a separate sale job completed by BreezyRealty. There are 7 variables, as follows:

job = job identifier

sales agent = sales agent identifier, taking values 1 through 4

time = total time in days taken to sell the listed property

type= type of the property sold, where 1=unit, 2=town house, and 3=house (this is what Stella means

by “type of property” in the quotation above)

satisfaction = response by owner of sold property when asked “Overall, how satisfied are you with your

property sale?” The coding is: Very dissatisfied = 1, Dissatisfied = 2, Satisfied = 3, Very Satisfied

= 4, and No response = 9

distance = estimated distance in meters of the sold property from the nearest beach

age = number of years since the property was built

Tutorials

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 Week 7 tutorials you will be asked to review the problem and to discuss with other

students what is required by way of analysis. You should download and save your project data as part of your

preparation for Week 7 tutorials. Also, use Excel to generate some descriptive statistics for your data, and

bring these to your tutorial in Week 7. This will confirm that you have correctly downloaded your data and

that are ready to commence your analysis. These descriptive statistics are also required to answer a Week 7

tutorial question.

In later weeks’ tutorials, 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: No longer than 1200 words, excluding tables and graphs. Total length including

all discussion, tables and graphs should not exceed 8 pages.

Cover sheet: Attach the project assessment sheet (given below) as your first page. Check

that your name and student ID have been completed. Do not use plastic

sheets or binders. Simply attach the completed cover sheet and staple the

pages together.

Project mark: The project will be marked out of 20, and will constitute 20% of your total

course mark.

Due date: A hard copy is to be submitted to your tutor in or before your scheduled

tutorial in week 11 (beginning October 12). In addition to the hard copy of the

project, you must also submit an electronic copy to the course website by

6pm on Friday 16 October. Upload your project via the Turnitin link on the

course website. Browse and upload a copy of your document - do not paste

text. Use your student ID in the file name (e.g., z1234567.doc).

Late submission: 20% of the value of the project will be deducted for every day late (including

the weekend). Late penalties apply to both the hard and electronic copy of the

project. 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.

Plagiarism: While you are encouraged to discuss your project with other students, each

person must submit an individual project that represents his or her own work.

All electronic versions of the projects 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.

Coverage: 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.

ECON1203 Business and Economic Statistics

PROJECT ASSESSMENT

Name ___________________________________________ zID __________________

Tutorial 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)