The data in the table below is 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 speeding up the process and eliminating some non-value-added
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.
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Get Help Now!Table: Insurance Claim Approval Times (days)
Old Process | New Process | |||
Week | Elapsed Time | Week | Elapsed Time | |
1 | 31.7 | 13 | 24 | |
2 | 27 | 14 | 25.8 | |
3 | 33.8 | 15 | 31 | |
4 | 30 | 16 | 23.5 | |
5 | 32.5 | 17 | 28.5 | |
6 | 33.5 | 18 | 25.6 | |
7 | 38.2 | 19 | 28.7 | |
8 | 37.5 | 20 | 27.4 | |
9 | 29 | 21 | 28.5 | |
10 | 31.3 | 22 | 25.2 | |
11 | 38.6 | 23 | 24.5 | |
12 | 39.3 | 24 | 23.5 |
Use the date in table above and answer the following questions
in the space provided below:
1. What was the average effect of the process change?
Did the process average increase or decrease and by how much?
2. 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.
3. How does this model measure the effect of the
process change?
4. 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.)
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