Gm 533 Final Paper free essay sample

Gm 533 Final Paper Executive Summary An analysis was performed for Quick Stab Collection Agency. This agency specializes on relatively small accounts and avoids risky collections such as debtor that tends to be chronically late with payments or is known to be hostile. The collection business can be very profitable. Quick Stab Collection Agency has been known to purchase small accounts for $10. 00 to collect a debt of $60. 00. The profitable of this agency depend critically on the numbers of days to collect the debt, the size of the bill and the discount rate offer. QSCA has asked us to find a relationship between the size of the bill and the days collected if any. In this data set there’s variable DAYS is the number to collect the payment, and also the BILL would be the amount of the overdue bills. The data will define that the TYPE=1 for residential accounts and the 0 for commercial accounts. A 95% confidence level was chosen. Introduction The strategy for QSCA Company it depends on how fast they can collect the debt and the amount of money received above what they paid for the account of course their intend is to get all of the amount but they realize that that may not those offering discounts in order to get payment. We will randomly select accounts from January to June that have been designated has overdue in the data set and try to establish a relationship between the size of the bill and the numbers of days to collect. In order to give management the tools need it to make decisions as to better handle accounts collections. Research Purpose The purpose of this analysis was to determine if there is a relationship between size of bills and days to collect the bill, But we have to ask the question how do we determine such a relationship if any and based to the data set given is their enough information to come to a conclusive decision. There is a sentiment with in QSCA that such a relation does exist. Hypothesis Analysis and Method Data Sample Having collected data for 6 months in both residential and commercial accounts we have a date set of 96 accounts both residential and commercial. The data set capture recorded dates the bill have been delinquent the amount of the bill and the type of bill. A copy of the survey can be found in the document sharing on the course website. Analysis Methodology In developing the criterion, a 95% confidence level was chosen and regression analysis along with a scatter plot. Computed Value of Test Statistic From the data set collected, the following test statistics were obtained: |Regression Analysis | | | | | | | | | | | | | | | |r? |0. 079 |n |32 | | | | |r |-0. 281 |k |1 | | | | |Std. Error |81. 430 |Dep. Var. |Bill | | | | | | | | | | | |ANOVA table | | | | | | | |Source |SS |df |MS |F |p-value | | |Regression | 17,097. 6041 |1 |17,097. 6041 |2. 58 |. 1188 | | |Residual | 198,925. 6146 |30 |6,630. 8538 | | | | |Total | 216,023. 2188 |31 | | | | | | | | | | | | | | | | | | | | | |Regression output | | | |confidence interval | |variables | coefficients |std. error | t (df=30) |p-value |95% lower |95% upper | |Intercept |219. 1587 |34. 7654 | 6. 304 |5. 98E-07 |148. 1582 |290. 1592 | | | |Descriptive statistics | | | | | |Days | |count |32 | |mean |53. 2 | |sample variance |604. 95 | |sample standard deviation |24. 60 | |minimum |13 | |maximum |94 | |range |81 | | | | |Descriptive statistics | | | | | |Bill | |count |32 | |mean |168. 34 | |sample variance |6,968. 49 | |sample standard deviation |83. 48 | |minimum |46 | |maximum |311 | |range |265 | | | | Scatter plot: [pic] Interpretation: Regression Analysis The r score of -0. 281 shows that our data a relatively has no strong positive correlation. The r score does not indicate that there is a positive relationship between the two variables. However the r square result was a result of 0. 079 wish would show only 7. 9% of the variance between the two data points. The standard error of 34. 7654 shows a relatively small sampling error in the data, or that there is too much error in the sample data. The F score of 2. 58 indicates that the linear relationship between the days and the bills isn’t particularly strong. The P value of 5. 98 is also too high to indicate a strong linear relationship. Descriptive Statistics We took a descriptive statistics for both data we used. The mean indicates that the days were 53 seconds. The range for the data set seems rather small at 81 seconds from 13 seconds to 94 seconds. The mean for bills was 168 or 16. 8 percent. This data also has a range of 265 or 26. 7 percent to a 31. 1 percent. Scatter Plot The scatter plot shows no indication between days and the bills. As you see that the day data decreases as the bill also decreases. Which they do not appears to have any variance from the line the days of the data points generally decreases with the bill data. Recommendation Conclusion References Bowerman, Bruce L. O’ Connell, Richard T, Orris, J. B; and Murphree, Emily S. 2010. Essentials of Business Statistics. New York: McGraw- Hill, Irwin . Microsolf Excel. Microsoft Corporation. Version 5. 0. 1997 Mega Stat Copyright 2009 by J. B Orris is a statistical add- in for Microsoft Excell, handcrafted by J. B Orris of Butler University. References Berenson, Mark L. , and David M. Levine. 1999. Basic Business Statistics. New Jersey: Prentice-Hall. Long, Lori. 1986. â€Å"Surveys from Start to Finish. † Info-Line 1-14. Microsoft( Excel. Microsoft Corporation. Version 5. 0. 1997. PHStat(. Prentice-Hall. 7th Edition. 1999.