Homework: The Data In The Table Are From A Study Conducted B

Homework the Data In The Table Are From A Study Conducted By An Insuran

The data in the table are from a study conducted by an insurance company to determine the effect of changing the process by which insurance claims are approved. The goal was to improve policyholder satisfaction by expediting the process and eliminating some extraneous approval steps in the process. The response measured was the average time required to approve and mail all claims initiated in a week. The new procedure was tested for 12 weeks, and the results were compared to the process performance for the 12 weeks prior to instituting the change. Use the data in the table and submit the answers to the following questions in a Word document: What was the average effect of the process change? Did the process average increase or decrease, and by how much? Analyze the data using the regression model y = b 0 + b 1 x , where y = time to approve and mail a claim (weekly average), x = 0 for the old process, and x = 1 for the new process. How does this model measure the effect of the process change? How much did the process performance change on the average? (Hint: Compare the values of b 1 and the average of new process performance minus the average of the performance of the old process.)

Paper For Above instruction

This analysis aims to evaluate the impact of a process change implemented by an insurance company on the average time required to approve and mail claims. By examining the data collected over two distinct periods—before and after the process change—we seek to quantify the effectiveness of the intervention in speeding up claims processing and enhancing customer satisfaction. The primary focus is to determine the average change in processing times and understand how the regression model y = b0 + b1 x facilitates this assessment.

Initially, the data indicates a period of 12 weeks where the old claims approval process was in place, followed by 12 weeks during which the new process was adopted. To assess the effect, we calculate the average processing time during these intervals. Suppose the average processing time during the pre-change period was denoted as Yold, and the average during the post-change period was Ynew. The difference (Ynew - Yold) provides a preliminary quantification of the change.

The regression model y = b0 + b1 x offers a more nuanced analysis by incorporating the process change as a binary variable, where x = 0 represents the old process and x = 1 the new. The intercept b0 estimates the average processing time under the old process, while the coefficient b1 estimates the change in average processing time attributable to the new process. Specifically, b1 quantifies the average difference in processing times between the two processes, effectively measuring the change introduced by the process modification.

Calculating these coefficients involves fitting the regression model to the data points collected during the 24-week period. Once fitted, the value of b1 directly represents the estimated average effect of changing from the old to the new process. If b1 is negative, it indicates a reduction in processing time; if positive, an increase. To verify this, we compare the regression coefficient to the actual difference between the average processing times before and after the change.

In conclusion, analyzing the data through this regression model allows us to precisely quantify the process change's impact. The expected outcome, based on typical implementation scenarios, is a decrease in the weekly average processing time, signifying improved efficiency. This methodology provides a clear and statistically grounded measure of the process improvement, which can inform future operational decisions and continuous improvement initiatives within the organization.

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