To complete the quiz, answer each question. Use the Excel template located under Instructor Insights to calculate the linear regression and the correlation coefficient.
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.
Please complete the calculations in excel or use a program of your choice and answer the following 14 questions in the assignment tab for this week. As always please watch the review videos provided in the media gallery.
Table: Insurance Claim Approval Times (days)
Use the date in table above and answer the following questions in the space provided below:
1. What is the linear regression equation for the old process?
2. Interpret what the slope in this equation means?
3. What is the linear regression equation for the new process?
4. Interpret what the slope in this equation means?
5. What is the interpretation of the y-intercept in the liner regression equation?
6. Comparing the old process to the new process was there an increase or decrease?
7. What is the correlation coefficient for the old process?
8. What is the correlation coefficient for the new process?
9. What is the coefficient of variation for the old process?
10. Interpret the coefficient of variation for the old process.
11. What is the coefficient of variation for the new process?
12. Interpret the coefficient of variation for the new process.
13. What was the average effect of the process change? Did the process average increase or decrease and by how much?
14. 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.)