Statistical Analysis for Quick Stab Collection Agency

Topics: Statistics, Prediction interval, Normal distribution Pages: 7 (1796 words) Published: January 15, 2012
Statistical Analysis for Quick Stab Collection Agency

Executive Summary
The purpose of the paper is to provide a statistical analysis of overdue bills for Quick Stab Collection Agency (QSCA). The data will be taken from accounts closed over a six month period. The goal is to determine if a correlation between the type of account, the amount of the bill and the days to collection exists. To determine the existence of a correlation, regression analysis of several variables was completed. This regression analysis also included predictions. Further study also included descriptive statistical analysis, together with graphs. This analysis will show that the correlation exists between the type of account and the days to collection. It will also show that the dollar amount the bill did not play a significant role in the days until collection. Introduction

Quick Stab Collection Agency (QSCA) is a bill collection agency specializing in small less risky accounts. QSCA buys the rights to collect debts from the original owner of the debt at a significant discount. The right to collect a $50 debt may be purchased for as little as $10. This example would indicate a profit of $40 not accounting for costs of doing business. Based on this example QSCA will need to be selective when purchasing debt as there the potential for profit loss with non-payment.

In order for QSCA to remain successful they must collect the greatest amount of payments in the shortest number of days. In an effort to accomplish this task, a review of 96 accounts which were closed out in the months of January through June was completed. This review provided the data set which will be used in this analysis.

This statistical analysis will attempt to answer the following questions: What is the average dollar amount for each account type over the six month period? What is the average collection time for each bill type?

Is there a correlation between the size of bill and the number of days until collection? Does the type of bill matter in terms of days until collection? Data
A random sample of accounts closed out during the months of January through June was provided. From this file titled OVERDUE (EXHIBIT A), we obtained the following: The number of days to collect the payment.

oThe number of days to collect the payment varied greatly across both account types. The minimum days until collection was 5 days and the maximum days until collection was 99 days. The amount of the overdue bill

oThe amount of bills also varied greatly across both account types. The minimum amount due was $46 and the maximum amount due was $311 Commercial account or Type 0
oThere were 48 commercial accounts present during the six month period analyzed Residential account or Type 1.
oThere were 48 residential accounts present during the six month period analyzed. oThis created an equal representation for the two account types present. Results
The average bill for both residential and commercial accounts was $174.27 during the months of January through June. oThe significance of the average being equal for both account types remains undetermined as the variables present were inadequate. The statistical model (Exhibit B) of the relationship between the type of bill, days late and the amount of the bill indicates that the type of account is statistically relevant to the days until collection. oAccount Type 0 higher relevance then Account Type 1

The statistical model indicates the following:
oThe residential account or Type 1 took an average of 31 days to collect. oThe commercial account or Type 0 took an average of 68 days to collect. oThe size of the bill does not relate to the number of days the bill is late. oType 0 accounts take longer to collect regardless of the dollar amount of the bill.

Generally speaking, the statistical model (Exhibit B) used would be considered valid as 63.5% of the variation in days is explained. As Exhibit B depicts...
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