risk

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exmple_6.docx

1. The Civil Air Patrol (CAP) owns airplanes used for search and rescue missions. In the course of their activities, CAP sustains losses in the form of property damages to their airplanes. Assume that the CAP covers two major regions in Pennsylvania– the Northeast (NE) and the Southwest (SW). The NE Region has 500 planes. The CAP has the following data for the number of accidents per airplane in the NE Region.

# Of accidents per airplane per year

# Of airplanes having this # of accidents

0

481

1

13

2

6

a. What is the random variable illustrated here?

Number of accidents per airplane per year is the random variable.

b. Derive the probability distribution based on this data. Be careful to label 
exactly what you are defining as the probability distribution.

# Of accidents per airplane per year

# Of airplanes having this # of accidents

Probability of airplanes having this # of accidents

0

481

481/500 =96.2%

1

13

13/500 =2.6%

2

6

6 /500 =1.2%

481+13+6 =500

There are 96.2% of airplanes having 0 accident per year.

There are 2.6% of airplanes having 1 accident per year.

There are 1.2% of airplanes having 2 accidents per year.

c. Calculate the expected value of frequency per airplane. Round your calculation to 4 decimal places. What are the units of measurement?

# Of accidents per airplane per year (1)

# of airplanes having this # of accidents (2)

Probability of airplanes having this # of accidents (3)

Total of accidents (4)

0

481

96.2%

0

1

13

2.6%

13

2

6

1.2%

12

500

100%

25

Expected value of frequency= 25/500 = 0.0500

The unit of measurement is expected value of the number of accident; it is the measure of central tendency. The expected value of frequency which is there are .05 accidents per airplane.

d. CAP has calculated the variance for the NE Region frequency distribution to be equal to 0.0715. Since you want to be sure you are using correct numbers in your evaluation, prove that CAP calculated the correct variance for frequency. Show all work! Round each calculation to 4 decimal places. You must use this validated variance (0.0715) for all further calculations. What are the units of measurement?

Outcome (1)

Mean (2)

(1)-(2)

((1)-(2))^2

Prob. Of Outcome

(4)*(5)

0

.05

-.05

.0025

.962

.0024

1

.05

.95

.9025

.026

.0235

2

.05

1.95

3.8025

.012

.0456

Mean=(0)(.962)+(1)(.026)+(2)(.012)=. 05

Variance=. 0024+. 0235+. 0456=. 0715

The units of measurements are number of airplanes and number of accidents; variance is the measure of disposition.

2. The CAP also has the following data for the number of accidents per airplane in the SW Region. The SW Region has 600 planes.

# Of accidents per airplane per year

# of airplanes having this # of accidents

0

576

1

15

2

9

You have had someone else check your calculations and are sure that these numbers are valid.

Mean =0.055

Variance =0.082

Which region is riskier, NE Region or SW Region? And why? Show all calculations and explain your numerical results.

Standard deviation for NE = (.0715)^(1/2) = .2674

Standard deviation for SW = (.082)^(1/2) = .2864

Coefficient of variance for NE = .2674/. 05 = 5.348

Coefficient of variance for SW = .2864/ .055 =5.2073

NE region faces more risk, because they have a higher coefficient of variance. Coefficient of variance = Standard deviation / mean

3. For the NE Region, assume initially that when accidents do occur, they are non- random and equal to $25,000.

a. Calculate the expected loss per airplane for NE Region.

Expected loss per airplane = E (f)*E (S) = (.05)*25,000 =1,250

b. Calculate the expected loss for ALL airplanes for NE Region.

(1,250)*(500)=625,000

4. Now based on the report in NE Region, CAP has constructed the following information related to the dollar amount of losses for 25 reported accidents.

a. Calculate the expected value of severity per airplane.

E (S) = 688,550/25 = 27,542

b. Calculated the expected loss per airplane.

Expected loss per airplane= E (f)*E (S)= (.05)*(27,542)=1,377.1

$ Amount of losses

# Of accidents having this $ amount of losses

Total $ losses

500

1

500

550

1

550

1000

2

2000

2500

1

2500

4000

1

4000

5000

1

5000

6000

1

6000

8000

1

8000

10000

2

20000

11000

1

11000

12000

2

24000

15000

2

30000

30000

1

30000

35000

1

35000

40000

1

40000

50000

1

50000

60000

2

120000

85000

1

85000

100000

1

100000

115000

1

115000

25

688500