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.

Due in Week 7 and worth 30 points

 

 

 

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.

 

 

 

            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?

 

  1. 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.

 

  1. How does this model measure the effect of the process change?

 

  1. 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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