Table is doownloaded in question aslo.
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 Elapsed Time New Process Elapsed Time     Week   Week     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 31.5 22 25.2   11 38.6 23 24.5   12 39.3 24 23.5    
Use the date 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 + b1 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 b1 and the average of new process performance minus the average of the performance of the old process.)