Course Project – Case 32
The purpose of this analysis is to assist the Quick Stab Collection Agency (QSCA) in determining if the amount or size of a bill collection is directly related to the number of days the bill is late. In order to support the validity of this relationship, a statistical analysis on the data provided will support the relationship with 95% confidence. These findings will give us a better understanding of the QSCA’s business and provide key insights on the relationships between the data being evaluated. Introduction:
Determining whether the amount of a bill has an effect on the number of days the bill is late is the key focal point of this analysis. This information will be valuable for the business to develop efficiency and profitability within the account services team. In addition, the final output of the analysis can be applied to many situations, such as insights into customer trends in bill payment, financing and the current economic impact on the bill collection business. This analysis will help confirm the importance of paying a bill on time and should be supported by the client services team in there management of bill acquisitions. We are currently faced with a challenging economy and our support of motivating clients to expedite their bill payments will help our business and our customer’s personal and internal finances. To validate the relationship between the amount of a bill and the number of days late it is for both commercial and residential accounts, we apply a linear regression method to generate an accurate statistical analysis of the data. By using this form of analysis, we will be able to answer the following questions with the information provided. * Does the size of the bill somehow relate to the number of days the payment is late? If so, how? * Does the model show the correlation between the size of the bill and the number of days the bill is late? * Does the relationship between days late and the amount of the bill differ between commercial and residential accounts?
Profitability at QSCA depends critically on the number of days to collect the payment and on the size of the bill, as well as on the discount rate offered. A random sample of 96 accounts closed out during the months of January through June yielded the following:
* The variable of DAYS for each account equals the number of days to collect the payment. * The variable BILL for each accounts, equals the amount of the overdue bill in dollars * TYPE – 1, indentifies residential accounts and TYPE - 0 identifies commercial accounts.
* By conducting a descriptive analysis on the data for both residential and commercial accounts, we find that the mean number for days late for a bill is approximately 50 days. The mean for the amount of bills due is approximately $174 dollars. * If we conduct a descriptive by customer type, we find that: * The mean days late for commercial accounts is approximately 68 day * The mean days late for residential accounts is approximately 31 days * The mean bill amount for both commercial and residential is the same * Three regression analyses were preformed for business accounts, residential accounts and combined. To visualize the data, a scatterplot was produced to visualize the data. Included in the scatter plot are the calculated regression equations and the r-squared values for each analysis is displayed – see appendix. * Per the tight linear grouping of data points in the regression analysis, we can determine that size of the bill does relate to the number of days the payment is late, * The relationship is positive for residential accounts, meaning a higher bill amount has an association with a larger number of days overdue. The linear regression model for these accounts is y = 5.630 x - 0.740, (y) is the amount of the bill and (x) is the number of days...
References: Bruce L. Bowerman, R. T. (2004). Essentials of Business Statistics. New York: McGraw-Hill Companies, Inc.
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