# pAYMENT TIME

Purpose of Assignment

The purpose of the assignment is to develop students’ abilities in using datasets to apply the concepts of sampling distributions and confidence intervals to make management decisions.

Assignment Steps

Resources:

Microsoft Excel®, The Payment Time Case Study, The Payment Time Case Data Set

Review

the Payment Time Case Study and Data Set.

(as an Introduction for your paper)

Develop

a minimum of 700-word report including the following calculations and using the information to determine whether the new billing system has reduced the mean bill payment time:

• Assuming the standard deviation of the payment times for all payments is 4.2 days, construct a 95% confidence interval estimate to determine whether the new billing system was effective. State the interpretation of 95% confidence interval and state whether or not the billing system was effective.
• Using the 95% confidence interval, can we be 95% confident that µ ≤ 19.5 days?
• Using the 99% confidence interval, can we be 99% confident that µ ≤ 19.5 days?
• If the population mean payment time is 19.5 days, what is the probability of observing a sample mean payment time of 65 invoices less than or equal to 18.1077 days?

Format

your assignment consistent with APA format.

References

: Minimum of 2 references and in text citation accordingly to the references you used.

Submit

: You need to submit 1 word document and 1 Excel file showing all the works done by you! calculations, graphs, tables need to be included in both the word document and the Excel file.

NOTE

: Attached are the needed resources for you to start this assignment. Make sure you compare your assignment with the attached grading guide to get full points for this

pAYMENT TIME
The Payment Time Case Grading Guide QNT/561 Version 9 The Payment Time Case Grading Guide QNT/561 Version 9 Applied Business Research and Statistics Individual Assignment: Expansion Strategy and Establishing a Re-order Point Purpose of Assignment The purpose of the assignment is to develop students’ abilities in using datasets to apply the concepts of sampling distributions and confidence intervals to make management decisions. Resources Required Microsoft Excel® Payment Time Case Study Payment Time Case Data Set Grading Guide Content Met Partially Met Not Met Comments: Review the Payment Time Case Study and Data Set.   Develop a 700-word report including the following calculations and using the information to determine whether the new billing system has reduced the mean bill payment time: • Assuming the standard deviation of the payment times for all payments is 4.2 days, construct a 95% confidence interval estimate to determine whether the new billing system was effective. State the interpretation of 95% confidence interval AND state whether or not the billing system was effective. • Using the 95% confidence interval, can we be 95% confident that µ ≤ 19.5 days? • Using the 99% confidence interval, can we be 99% confident that µ ≤ 19.5 days? • If the population mean payment time is 19.5 days, what is the probability of observing a sample mean payment time of 65 invoices less than or equal to 18.1077 days? Total Available Total Earned Writing Guidelines Met Partially Met Not Met Comments: The paper—including tables and graphs, headings, title page, and reference page—is consistent with APA formatting guidelines and meets course-level requirements. Intellectual property is recognized with in-text citations and a reference page. Paragraph and sentence transitions are present, logical, and maintain the flow throughout the paper. Sentences are complete, clear, and concise. Rules of grammar and usage are followed including spelling and punctuation. Total Available Total Earned   Assignment Total # Additional comments:
pAYMENT TIME
Case Study – Payment Time Case Study QNT/561 Version 9 Case Study – Payment Time Case Study Major consulting firms such as Accenture, Ernst & Young Consulting, and Deloitte & Touche Consulting employ statistical analysis to assess the effectiveness of the systems they design for their customers. In this case, a consulting firm has developed an electronic billing system for a Stockton, CA, trucking company. The system sends invoices electronically to each customer’s computer and allows customers to easily check and correct errors. It is hoped the new billing system will substantially reduce the amount of time it takes customers to make payments. Typical payment times—measured from the date on an invoice to the date payment is received—using the trucking company’s old billing system had been 39 days or more. This exceeded the industry standard payment time of 30 days. The new billing system does not automatically compute the payment time for each invoice because there is no continuing need for this information. The management consulting firm believes the new system will reduce the mean bill payment time by more than 50 percent. The mean payment time using the old billing system was approximately equal to, but no less than, 39 days. Therefore, if µ denotes the new mean payment time, the consulting firm believes that µ will be less than 19.5 days. Therefore, to assess the system’s effectiveness (whether µ < 19.5 days), the consulting firm selects a random sample of 65 invoices from the 7,823 invoices processed during the first three months of the new system’s operation. Whereas this is the ﬁrst time the consulting company has installed an electronic billing system in a trucking company, the ﬁrm has installed electronic billing systems in other types of companies. Analysis of results from these other companies show, although the population mean payment time varies from company to company, the population standard deviation of payment times is the same for different companies and equals 4.2 days. The payment times for the 65 sample invoices are manually determined and are given in the Excel® spreadsheet named “The Payment Time Case”. If this sample can be used to establish that new billing system substantially reduces payment times, the consulting firm plans to market the system to other trucking firms.