# Analysis of Quick Stab Collection Agency

Table of Content

## Executive Summary:

The purpose of the analysis is to assist Quick Stab Collection Agency (QSCA) in determining if there is a correlation between the size of a bill collection and how many days it is overdue. To confirm this relationship, statistical analysis will be performed on the given data at a 95% confidence level. The results will improve our comprehension of QSCA’s operations and offer valuable insights into the connections within the evaluated data.

Determining if the amount of a bill affects its lateness is the main focus of this analysis. This information is useful for the business to improve efficiency and profits in account services. Additionally, the analysis results can be applied to various situations, including understanding customer bill payment trends, financing, and the economic impact on bill collection. The analysis reinforces the significance of timely bill payment and should be supported by the client services team in managing bill acquisitions.

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In this challenging economy, we advise clients to accelerate their bill payments for the benefit of our business and our customers’ personal and internal finances. To accurately analyze the data and establish a link between a bill’s amount and its lateness, we employ a linear regression method. Through this examination, we aim to address several inquiries such as: Does the size of a bill have any connection with the number of days it is late? If so, what is that connection? Does our model show any correlation between the size of a bill and lateness? Moreover, we seek to determine if there are distinct relationships between days late and bill amount for commercial and residential accounts.

## Data Provided:

The profitability of QSCA relies heavily on the number of days it takes to collect payment and the size of the bill, along with the discount rate offered. During the months of January through June, a random sample of 96 accounts that were closed out revealed the following information: The variable “DAYS” represents the number of days it took to collect payment for each account. The variable “BILL” represents the amount of the overdue bill in dollars. Accounts with a TYPE of 1 are residential accounts and those with a TYPE of 0 are commercial accounts.

## Results:

By conducting a descriptive analysis on the data for both residential and commercial accounts, we find that the average number of days a bill is late is around 50 days. The average bill amount is approximately \$174 dollars. When analyzing customer type separately, commercial accounts have an average of 68 days late for their bills, while residential accounts have an average of 31 days late. However, the average bill amount is the same for both commercial and residential accounts. We also performed three regression analyses for business accounts, residential accounts, and the combined data.

The scatterplot visualized the data and included the regression equations and r-squared values, which can be found in the appendix. Analyzing the close grouping of data points in regression shows that there is a positive correlation between bill size and overdue days. However, this correlation only applies to residential accounts, suggesting that larger bill amounts are associated with longer payment delays.

The linear regression model for these accounts can be expressed as y = 5.630x – 0.740 for regular accounts and y = -5.009x + 517.274 for commercial accounts, where (y) represents the amount of the bill and (x) represents the number of days overdue. For regular accounts, there is a positive relationship between the number of days overdue and the bill amount, with every one day increase resulting in a \$5.63 increase in the bill. On the other hand, for commercial accounts, there is a negative relationship between days overdue and bill amount, meaning that as the number of days overdue decreases by one day, there is a corresponding decrease in the bill amount by \$5.01.

The correlation is highly significant for both residential accounts (r2 = 0. 933 or 93. 3% explained variation) and commercial accounts (r2 = 0. 957 or 95. 7% explained variation). Bills in the range of \$250 – \$300 tend to be approximately 50 days overdue for both types of accounts. Residential accounts with bills below \$250 are likely to be paid prior to the 50-day mark. Conversely, commercial accounts with bills less than \$250 generally settle their payment after the 50-day period.

## Recommendations:

Based on the regression analysis data, it is recommended that management enhances the training of account managers to encourage clients to pay their bills promptly. Account managers can motivate clients by offering them the option to benefit from discount rates in order to accelerate their payment process.

It is recommended that we prioritize commercial accounts as there is a greater opportunity to increase the slope on the regression line in order to resolve late payments on low bill amounts. Recent economic trends make it more challenging to improve payments for high balance residential accounts. However, it is still crucial to offer discount rates to expedite payment. The account management team should establish goals to reduce the number of days bills are overdue for both commercial and residential accounts. This will enhance profitability for the Quick Stab Collection Agency.

## Conclusions and Summary:

By analyzing the data, we have discovered that there is a direct correlation between the amount of a bill and the number of days it is overdue. This correlation is evident in both residential and business accounts. Residential accounts tend to have outstanding bill amounts ranging from \$50 to \$300, while being approximately 0 to 50 days late. We observed that higher delinquency bills were linked to larger account balances due, whereas smaller account balances were associated with bills that were less overdue.

Commercial accounts appeared to have outstanding balances ranging from \$50 to \$300, with longer payment delays of 40 to 100 days. It was observed that smaller balances were more likely to have overdue bills, while larger balances showed less tardiness. It is worth noting that the smallest account, with a balance of \$60, was 99 days late. This suggests a potential opportunity for QSCA to expedite payment for low commercial account balances. However, these findings do not necessarily indicate a correlation between the size of a bill and payment lateness.

The analysis indicates a correlation between the bill size and the number of days overdue in a considerable customer base. Late payment of bills can be influenced by various factors, necessitating further research to expand upon these discoveries.

## References:

Bruce L. Bowerman, R. T. (2004). Essentials of Business Statistics. New York: McGraw-Hill Companies, Inc.