Complete The Following Questions And Attached Excel Workbook

Complete The Following Questions And Attached Excel Workbookuse The D

Complete the following questions and attached excel workbook. Use the data in the table above and answer the following questions in the space provided below: 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 = b0 + b1x, 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 b1 and the average of new process performance minus the average of the performance of the old process.)

Paper For Above instruction

The process improvement initiative, demonstrated through a regression analysis, provides critical insights into the effectiveness of the process change. By analyzing the data provided in the attached Excel workbook, we can assess the impact of transitioning from the old process to the new process on the average time to approve and mail a claim.

The core of the analysis involves calculating the average performance metrics before and after the process change. Specifically, the mean time to approve and mail claims under the old process (represented by x=0) and the new process (represented by x=1). These averages serve as baseline indicators of performance and help us quantify the impact of the change.

Using the regression model y = b0 + b1x, where y represents the weekly average time to process claims, the coefficient b0 indicates the estimated average processing time under the old process, and b1 measures the change attributable to the new process. Essentially, b1 captures the difference in process performance due to the change in methodology.

To interpret the effect of the process change, we examine the value of b1 relative to the difference in the average performance between the two processes. A negative value of b1 signifies an improvement—reducing the average processing time—while a positive value would suggest a deterioration.

Calculating the averages from the data shows that the old process had an average processing time of X days, while the new process averaged Y days. The difference (Y - X) illustrates the actual change in performance. Correspondingly, the regression coefficient b1 should approximate this difference, validating the model’s accuracy.

In conclusion, the regression analysis indicates that the process change resulted in a decrease/increase of Z days in the average processing time. This measurable improvement/deterioration quantifies the effectiveness of the process change. Decision-makers can use this information to confirm the benefits of the new process or identify further areas for improvement.

References

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