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QHA, Applied Statistics
Your employer, Quick Hit Agency (QHA), is a debt collections agency. The company specializes in collecting small accounts. QHA does not deal in large accounts and does not take on risky collections, such as those in which the debtor tends to be chronically late in payments or is known to be hostile.
To distinguish itself from competing collection agencies, the company wants to establish a reputation for collecting delinquent accounts fast. The marketing department has just suggested that QHA adopt the slogan:
“Under 60 days or your money back!“
At the last staff meeting in which this slogan was proposed, the marketing department was asked how they arrived at the number 60. You found the reply unsatisfactory at best: “Well, 60 sounded like a nice round number.”
Since you work as an accountant at QHA, you have access to account balances. You suspect that the number of days to collect the payment is related to the size of the bill. If this is the case, you may be able to estimate how quickly certain accounts are likely to be collected, which, in turn, may assist the marketing department in determining an appropriate level for the moneyback guarantee.
To test this theory, you’ve taken a sample of accounts closed out during the months of January through June. The data set includes the initial size of the account and the total number of days to collect payment in full. QHA deals in both residential and commercial accounts and you’ve collected data from both groups. The first 48 observations in the data set are residential accounts and the second 48 are commercial accounts. QHA has approximately equal numbers of residential and commercial accounts so the samples are of the right size.
In the worksheet, the variable Late is the number of days the payment was overdue, and the variable Bill is the amount of the bill in dollars.
Data Analysis & Statistical Modeling for Business 6
Your submission should be in the form of a report commencing with an Executive Summary which includes a recommendation on the feasibility of the 60 day money back guarantee. The next section should be titled Exploratory Analysis and provide an initial examination of the data. The final section titled Regression Analysis should decide whether the size of the bill a useful predictor of the number of days the payment is overdue.
You should make effective use of StatToolsTM output. Results should be reported using the appropriate templates.
Please DO NOT remove any data.
Page limit: Maximum 7 pages including all diagrams
Posted Date: 6/30/2013 5:26:31 AM  Location : Australia
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