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Case Study Transforming Data into Information
Case Study: Transforming Data into Information
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Case Study: Transforming Data into Information
This case study focused on producing a report to demonstrate the ability to analyze data both graphically and numerically. The case study was based on the data obtained from the Regional Call Center’s Washington, DC. Regional is a company that provides contract call center services to various companies in the US, including major retail organizations, and banks, among others. As the company’s supervisor and having worked with the company for seven years, I was required to prepare a report describing the calls that the company handled for one of the Regional’s clients. Thus, I was provided with a data file consisting of 57 calls and with the following variables: account number, past due amount, current account balance, and nature of the call.
Therefore, to produce the report, the data were analyzed both graphically and numerically using Excel. For instance, to investigate whether there was a difference in the mean and median current account balance, a bar chart was produced (Figure 1). The results showed that the average current account balance was greater than the median current account balance. Since the mean was greater than the median, then the distribution of the current account balance was positively skewed (Stephanie, 2022). This implies most of the current account balances were equal to or greater than the mean.
Figure 1. Bar chart of the mean and median current balance
In addition, to understand the relationship between current account balance and past due amount, a scatter plot was used (Figure 2). A scatter plot helps a researcher to understand the direction and strength of the linear relationship between variables (“What is a scatter plot and when to use one,” 2020). The scatter plot showed a weak positive linear relationship between current balance and past due. This implies that when the current account balance increases, the past due amount also increases. Conversely, when the current account balances decrease, the past due amount also decreases.
Figure 2. Scatter plot of current balance vs. past due
Moreover, descriptive statistics were computed to further describe the current account balance and past due amount (Table 1). Descriptive statistics are useful in research because they help in describing the fundamental characteristics of the data (Trochim, n.d.). In addition, when combined with simple graphics, they help in gaining insights into the data. Based on the results, the highest current amount due was 323.78. On the other hand, the lowest current amount due was -129.67. The average current amount due was 62.70, while the median was 57.54. Since the mean was slightly greater than the median, then the current amount due data was skewed to the right (positive skewness). The standard deviation was 75.58, which is very large. This implies that there was high variability in the data. This high variance is a good factor that can attract aggressive investors, who are less risk averse. However, the high variance could discourage conservative investors who have less risk tolerance (“Is variance good or bad for stock investors?” 2015). In fact, the coefficient of variation was 1.21, which further supports that the variance was extremely high to attract investors.
For the past due amount, descriptive results showed that the minimum was zero, while the maximum was 386.77. This indicates the highest amount required to make the bank account current again. The results also revealed that the average past due amount was 23.95 and a median value of zero. This implies that the past due amount of data was skewed to the right (positive skewness). The standard deviation was 61.67, which was very high. This high variability could discourage conservative investors with less risk tolerance.
Table 1: Descriptive Statistics (Current Amount Due and Past Due Amount)
Current Amount Due Past Due Amount
Mean 62.6982456 Mean 23.9464912
Standard Error 10.010904 Standard Error 8.16830585
Median 57.54 Median 0
Mode 0 Mode 0
Standard Deviation 75.5806676 Standard Deviation 61.6693568
Sample Variance 5712.43732 Sample Variance 3803.10957
Kurtosis 1.99588201 Kurtosis 22.0569587
Skewness 0.56451104 Skewness 4.29303586
Range 453.45 Range 386.77
Minimum -129.67 Minimum 0
Maximum 323.78 Maximum 386.77
Sum 3573.8 Sum 1364.95
Count 57 Count 57
The descriptive statistics for the past due balances were also computed (Table 2). The variable was derived as the difference between the current account amount and the past due amount. The descriptive statistics showed that the maximum past-due balance was 323.78. The maximum value indicates that the past due balance was 323.78 above the current due balance. On the other hand, the minimum past-due balance was -386.77. The minimum value indicates that the worst account was 386.77 below the current balance. The average past-due balance was 38.75. This implies that on average, the bank was above the current balance by 38.75. However, the standard deviation was 86.86, which was very high. This implies that the past-due balance was very unreliable for investors.
Table 2: Descriptive Statistics (Past Due Balance)
Past due balance
Mean 38.7517544
Standard Error 11.504976
Median 50.79
Mode 0
Standard Deviation 86.8606639
Sample Variance 7544.77493
Kurtosis 11.3810395
Skewness -1.6611254
Range 710.55
Minimum -386.77
Maximum 323.78
Sum 2208.85
Count 57
In conclusion, the results revealed that the average current account amount was higher than the median. This implies most of the bank’s accounts had a current amount equal to or above the mean. However, descriptive statistics indicated that the current amount due had high variance, indicating that there was high variability in the current amount balance for the 57 calls. Regarding the past due amount, the results revealed that the amount was 386.77 above the current amount due. In addition, the mean was higher than the median, indicating that the distribution of past due amounts was positively skewed. This further implies that, on average, the past due amount for most of the calls was greater or equal to the mean. However, the standard deviation in past due amounts indicates that the company was unreliable for investment, especially those that are less risk tolerant. This case study also analyzed the past due balance using descriptive statistics. The results showed that the worst account was 386.77 below the current balance. In addition, there was high variability in the past due balance indicating that the 57 calls were generally unreliable for less-risk tolerance investors.
Moreover, this study investigated the relationship between the past amount due and the current amount due. The results showed a weak and positive linear relationship between the variables. This implies that when the current amount due increases, the past due amount also increases. Conversely, when the current amount due decreases, the past due amount also decreases. Thus, for the company to reduce the past due amount, it should encourage investors to increase the current amount due. In addition, the company should focus on investors who are aggressive investors, who are less risk averse.
References
Stephanie. (2022, January 13). Pearson mode skewness. Retrieved from https://www.statisticshowto.com/pearson-mode-skewness/What is a scatter plot and when to use one. (2020, July 10). Retrieved from https://visme.co/blog/scatter-plot/Trochim, W. M. (n.d.). Descriptive statistics. Retrieved from https://conjointly.com/kb/descriptive-statistics/
Is variance good or bad for stock investors? (2015, April 14). Retrieved from https://www.investopedia.com/ask/answers/041415/variance-good-or-bad-stock-investors.asp
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