# MAT510 Study Conducted by an Insurance Company

*label*Mathematics

*timer*Asked: Feb 17th, 2019

*account_balance_wallet*$20

### Question Description

**!!!!AN EXCEL SPREADSHEET SHOULD ALSO ACCOMPANY THE ANSWERS!!!!**

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.

Use the date in 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.)

## Tutor Answer

Attached.

Old Process

Week

Elapsed Time

1

31.7

2

27

3

33.8

4

30

5

32.5

6

33.5

7

38.2

8

37.5

9

29

10

31.3

11

38.6

12

39.3

Average

33.53

x

0

0

0

0

0

0

0

0

0

0

0

0

New Process

Week

Elapsed Time

13

24

14

25.8

15

31

16

23.5

17

28.5

18

25.6

19

28.7

20

27.4

21

28.5

22

25.2

23

24.5

24

23.5

Average

26.35

x

1

1

1

1

1

1

1

1

1

1

1

1

Regression Statistics

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.746136076

R Square

0.556719044

Adjusted R Square

0.536569909

Standard Error

3.347432304

Observations

24

ANOVA

df

Regression

Residual

Total

Intercept

X Variable 1

SS

309.6016667

246.5166667

225.1183333

MS

309.6017

11.2053

Coefficients

Standard Error

33.53333333

0.966320471

7.183333333

1.366583516

t Stat

34.70208

-5.25642

1

22

23

F

Significance F

27.62992

2.84E-01

P-value

1.05E-20

2.84E-05

Lower 95%

31.52930733

10.01745408

Upper 95%

35.53735933

4.349212585

MAT 510 – Homework Assignment

Homework Assignment 6

Due in Week 7 and worth 30 points

The data in the t...

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