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SOCIETY OF ACTUARIES
EXAM P PROBABILITY
EXAM P SAMPLE QUESTIONS
Copyright 2016 by the Society of Actuaries
Some of the questions in this study note are taken from past examinations.
Some of the questions have been reformatted from previous versions of this note.
Questions 154-155 were added in October 2014.
Questions 156-206 were added January 2015.
Questions 207-237 were added April 2015.
Questions 238-240 were added May 2015.
Questions 241-242 were added November 2015.
Questions 243-326 were added September 2016.
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1. A survey of a group’s viewing habits over the last year revealed the following
information:
(i) 28% watched gymnastics
(ii) 29% watched baseball
(iii) 19% watched soccer
(iv) 14% watched gymnastics and baseball
(v) 12% watched baseball and soccer
(vi) 10% watched gymnastics and soccer
(vii) 8% watched all three sports.
Calculate the percentage of the group that watched none of the three sports
during the last year.
(A) 24%
(B) 36%
(C) 41%
(D) 52%
(E) 60%
2. The probability that a visit to a primary care physician’s (PCP) office results in neither
lab work nor referral to a specialist is 35%. Of those coming to a PCP’s office, 30% are
referred to specialists and 40% require lab work.
Calculate the probability that a visit to a PCP’s office results in both lab work and referral
to a specialist.
(A) 0.05
(B) 0.12
(C) 0.18
(D) 0.25
(E) 0.35
3. You are given [ ] 0.7P A B  and [ ] 0.9P A B  .
Calculate P[A].
(A) 0.2
(B) 0.3
(C) 0.4
(D) 0.6
(E) 0.8
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4. An urn contains 10 balls: 4 red and 6 blue. A second urn contains 16 red balls and an
unknown number of blue balls. A single ball is drawn from each urn. The probability
that both balls are the same color is 0.44.
Calculate the number of blue balls in the second urn.
(A) 4
(B) 20
(C) 24
(D) 44
(E) 64
5. An auto insurance company has 10,000 policyholders. Each policyholder is classified as
(i) young or old;
(ii) male or female; and
(iii) married or single.
Of these policyholders, 3000 are young, 4600 are male, and 7000 are married. The
policyholders can also be classified as 1320 young males, 3010 married males, and 1400
young married persons. Finally, 600 of the policyholders are young married males.
Calculate the number of the company’s policyholders who are young, female, and single.
(A) 280
(B) 423
(C) 486
(D) 880
(E) 896
6. A public health researcher examines the medical records of a group of 937 men who died
in 1999 and discovers that 210 of the men died from causes related to heart disease.
Moreover, 312 of the 937 men had at least one parent who suffered from heart disease,
and, of these 312 men, 102 died from causes related to heart disease.
Calculate the probability that a man randomly selected from this group died of causes
related to heart disease, given that neither of his parents suffered from heart disease.
(A) 0.115
(B) 0.173
(C) 0.224
(D) 0.327
(E) 0.514
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7. An insurance company estimates that 40% of policyholders who have only an auto policy
will renew next year and 60% of policyholders who have only a homeowners policy will
renew next year. The company estimates that 80% of policyholders who have both an
auto policy and a homeowners policy will renew at least one of those policies next year.
Company records show that 65% of policyholders have an auto policy, 50% of
policyholders have a homeowners policy, and 15% of policyholders have both an auto
policy and a homeowners policy.
Using the company’s estimates, calculate the percentage of policyholders that will renew
at least one policy next year.
(A) 20%
(B) 29%
(C) 41%
(D) 53%
(E) 70%
8. Among a large group of patients recovering from shoulder injuries, it is found that 22%
visit both a physical therapist and a chiropractor, whereas 12% visit neither of these. The
probability that a patient visits a chiropractor exceeds by 0.14 the probability that a
patient visits a physical therapist.
Calculate the probability that a randomly chosen member of this group visits a physical
therapist.
(A) 0.26
(B) 0.38
(C) 0.40
(D) 0.48
(E) 0.62
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9. An insurance company examines its pool of auto insurance customers and gathers the
following information:
(i) All customers insure at least one car.
(ii) 70% of the customers insure more than one car.
(iii) 20% of the customers insure a sports car.
(iv) Of those customers who insure more than one car, 15% insure a sports car.
Calculate the probability that a randomly selected customer insures exactly one car and
that car is not a sports car.
(A) 0.13
(B) 0.21
(C) 0.24
(D) 0.25
(E) 0.30
10. Question duplicates Question 9 and has been deleted.
11. An actuary studying the insurance preferences of automobile owners makes the following
conclusions:
(i) An automobile owner is twice as likely to purchase collision coverage as
disability coverage.
(ii) The event that an automobile owner purchases collision coverage is
independent of the event that he or she purchases disability coverage.
(iii) The probability that an automobile owner purchases both collision and
disability coverages is 0.15.
Calculate the probability that an automobile owner purchases neither collision nor
disability coverage.
(A) 0.18
(B) 0.33
(C) 0.48
(D) 0.67
(E) 0.82
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12. A doctor is studying the relationship between blood pressure and heartbeat abnormalities
in her patients. She tests a random sample of her patients and notes their blood pressures
(high, low, or normal) and their heartbeats (regular or irregular). She finds that:
(i) 14% have high blood pressure.
(ii) 22% have low blood pressure.
(iii) 15% have an irregular heartbeat.
(iv) Of those with an irregular heartbeat, one-third have high blood pressure.
(v) Of those with normal blood pressure, one-eighth have an irregular
heartbeat.
Calculate the portion of the patients selected who have a regular heartbeat and low blood
pressure.
(A) 2%
(B) 5%
(C) 8%
(D) 9%
(E) 20%
13. An actuary is studying the prevalence of three health risk factors, denoted by A, B, and C,
within a population of women. For each of the three factors, the probability is 0.1 that a
woman in the population has only this risk factor (and no others). For any two of the
three factors, the probability is 0.12 that she has exactly these two risk factors (but not the
other). The probability that a woman has all three risk factors, given that she has A and
B, is 1/3.
Calculate the probability that a woman has none of the three risk factors, given that she
does not have risk factor A.
(A) 0.280
(B) 0.311
(C) 0.467
(D) 0.484
(E) 0.700
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14. In modeling the number of claims filed by an individual under an automobile policy
during a three-year period, an actuary makes the simplifying assumption that for all
integers n  0, ( 1) 0.2 ( )p n p n  where ( )p n represents the probability that the
policyholder files n claims during the period.
Under this assumption, calculate the probability that a policyholder files more than one
claim during the period.
(A) 0.04
(B) 0.16
(C) 0.20
(D) 0.80
(E) 0.96
15. An insurer offers a health plan to the employees of a large company. As part of this plan,
the individual employees may choose exactly two of the supplementary coverages A, B,
and C, or they may choose no supplementary coverage. The proportions of the
company’s employees that choose coverages A, B, and C are 1/4, 1/3, and 5/12
respectively.
Calculate the probability that a randomly chosen employee will choose no supplementary
coverage.
(A) 0
(B) 47/144
(C) 1/2
(D) 97/144
(E) 7/9
16. An insurance company determines that N, the number of claims received in a week, is a
random variable with 1
1
[ ]
2n
P N n 
  where 0n  . The company also determines that
the number of claims received in a given week is independent of the number of claims
received in any other week.
Calculate the probability that exactly seven claims will be received during a given
two-week period.
(A) 1/256
(B) 1/128
(C) 7/512
(D) 1/64
(E) 1/32
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17. An insurance company pays hospital claims. The number of claims that include
emergency room or operating room charges is 85% of the total number of claims. The
number of claims that do not include emergency room charges is 25% of the total number
of claims. The occurrence of emergency room charges is independent of the occurrence
of operating room charges on hospital claims.
Calculate the probability that a claim submitted to the insurance company includes
operating room charges.
(A) 0.10
(B) 0.20
(C) 0.25
(D) 0.40
(E) 0.80
18. Two instruments are used to measure the height, h, of a tower. The error made by the
less accurate instrument is normally distributed with mean 0 and standard deviation
0.0056h. The error made by the more accurate instrument is normally distributed with
mean 0 and standard deviation 0.0044h.
The errors from the two instruments are independent of each other.
Calculate the probability that the average value of the two measurements is within 0.005h
of the height of the tower.
(A) 0.38
(B) 0.47
(C) 0.68
(D) 0.84
(E) 0.90
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19. An auto insurance company insures drivers of all ages. An actuary compiled the
following statistics on the company’s insured drivers:
Age of
Driver
Probability
of Accident
Portion of Company’s
Insured Drivers
16-20
21-30
31-65
66-99
0.06
0.03
0.02
0.04
0.08
0.15
0.49
0.28
A randomly selected driver that the company insures has an accident.
Calculate the probability that the driver was age 16-20.
(A) 0.13
(B) 0.16
(C) 0.19
(D) 0.23
(E) 0.40
20. An insurance company issues life insurance policies in three separate categories:
standard, preferred, and ultra-preferred. Of the company’s policyholders, 50% are
standard, 40% are preferred, and 10% are ultra-preferred. Each standard policyholder
has probability 0.010 of dying in the next year, each preferred policyholder has
probability 0.005 of dying in the next year, and each ultra-preferred policyholder
has probability 0.001 of dying in the next year.
A policyholder dies in the next year.
Calculate the probability that the deceased policyholder was ultra-preferred.
(A) 0.0001
(B) 0.0010
(C) 0.0071
(D) 0.0141
(E) 0.2817
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21. Upon arrival at a hospital’s emergency room, patients are categorized according to their
condition as critical, serious, or stable. In the past year:
(i) 10% of the emergency room patients were critical;
(ii) 30% of the emergency room patients were serious;
(iii) the rest of the emergency room patients were stable;
(iv) 40% of the critical patients died;
(vi) 10% of the serious patients died; and
(vii) 1% of the stable patients died.
Given that a patient survived, calculate the probability that the patient was categorized as
serious upon arrival.
(A) 0.06
(B) 0.29
(C) 0.30
(D) 0.39
(E) 0.64
22. A health study tracked a group of persons for five years. At the beginning of the study,
20% were classified as heavy smokers, 30% as light smokers, and 50% as nonsmokers.
Results of the study showed that light smokers were twice as likely as nonsmokers to die
during the five-year study, but only half as likely as heavy smokers.
A randomly selected participant from the study died during the five-year period.
Calculate the probability that the participant was a heavy smoker.
(A) 0.20
(B) 0.25
(C) 0.35
(D) 0.42
(E) 0.57
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23. An actuary studied the likelihood that different types of drivers would be involved in at
least one collision during any one-year period. The results of the study are:
Type of
driver
Percentage of
all drivers
Probability
of at least one
collision
Teen
Young adult
Midlife
Senior
8%
16%
45%
31%
0.15
0.08
0.04
0.05
Total 100%
Given that a driver has been involved in at least one collision in the past year, calculate
the probability that the driver is a young adult driver.
(A) 0.06
(B) 0.16
(C) 0.19
(D) 0.22
(E) 0.25
24. The number of injury claims per month is modeled by a random variable N with
1
[ ]
( 1)( 2)
P N n
n n
 
 
, for nonnegative integers, n.
Calculate the probability of at least one claim during a particular month, given that there
have been at most four claims during that month.
(A) 1/3
(B) 2/5
(C) 1/2
(D) 3/5
(E) 5/6
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25. A blood test indicates the presence of a particular disease 95% of the time when the
disease is actually present. The same test indicates the presence of the disease 0.5% of
the time when the disease is not actually present. One percent of the population actually
has the disease.
Calculate the probability that a person actually has the disease given that the test indicates
the presence of the disease.
(A) 0.324
(B) 0.657
(C) 0.945
(D) 0.950
(E) 0.995
26. The probability that a randomly chosen male has a blood circulation problem is 0.25.
Males who have a blood circulation problem are twice as likely to be smokers as those
who do not have a blood circulation problem.
Calculate the probability that a male has a blood circulation problem, given that he is a
smoker.
(A) 1/4
(B) 1/3
(C) 2/5
(D) 1/2
(E) 2/3
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27. A study of automobile accidents produced the following data:
Model
year
Proportion of
all vehicles
Probability of
involvement
in an accident
2014 0.16 0.05
2013 0.18 0.02
2012 0.20 0.03
Other 0.46 0.04
An automobile from one of the model years 2014, 2013, and 2012 was involved in an
accident.
Calculate the probability that the model year of this automobile is 2014.
(A) 0.22
(B) 0.30
(C) 0.33
(D) 0.45
(E) 0.50
28. A hospital receives 1/5 of its flu vaccine shipments from Company X and the remainder
of its shipments from other companies. Each shipment contains a very large number of
vaccine vials.
For Company X’s shipments, 10% of the vials are ineffective. For every other company,
2% of the vials are ineffective. The hospital tests 30 randomly selected vials from a
shipment and finds that one vial is ineffective.
Calculate the probability that this shipment came from Company X.
(A) 0.10
(B) 0.14
(C) 0.37
(D) 0.63
(E) 0.86
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29. The number of days that elapse between the beginning of a calendar year and the moment
a high-risk driver is involved in an accident is exponentially distributed. An insurance
company expects that 30% of high-risk drivers will be involved in an accident during the
first 50 days of a calendar year.
Calculate the portion of high-risk drivers are expected to be involved in an accident
during the first 80 days of a calendar year.
(A) 0.15
(B) 0.34
(C) 0.43
(D) 0.57
(E) 0.66
30. An actuary has discovered that policyholders are three times as likely to file two claims
as to file four claims.
The number of claims filed has a Poisson distribution.
Calculate the variance of the number of claims filed.
(A)
1
3
(B) 1
(C) 2
(D) 2
(E) 4
31. A company establishes a fund of 120 from which it wants to pay an amount, C, to any of
its 20 employees who achieve a high performance level during the coming year. Each
employee has a 2% chance of achieving a high performance level during the coming
year. The events of different employees achieving a high performance level during the
coming year are mutually independent.
Calculate the maximum value of C for which the probability is less than 1% that the fund
will be inadequate to cover all payments for high performance.
(A) 24
(B) 30
(C) 40
(D) 60
(E) 120
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32. A large pool of adults earning their first driver’s license includes 50% low-risk drivers,
30% moderate-risk drivers, and 20% high-risk drivers. Because these drivers have no
prior driving record, an insurance company considers each driver to be randomly selected
from the pool.
This month, the insurance company writes four new policies for adults earning their first
driver’s license.
Calculate the probability that these four will contain at least two more high-risk drivers
than low-risk drivers.
(A) 0.006
(B) 0.012
(C) 0.018
(D) 0.049
(E) 0.073
33. The loss due to a fire in a commercial building is modeled by a random variable X with
density function
0.005(20 ), 0 20
( )
0, otherwise.
x x
f x
  
 

Given that a fire loss exceeds 8, calculate the probability that it exceeds 16.
(A) 1/25
(B) 1/9
(C) 1/8
(D) 1/3
(E) 3/7
34. The lifetime of a machine part has a continuous distribution on the interval (0, 40) with
probability density function f(x), where f(x) is proportional to (10 + x) 2
on the interval.
Calculate the probability that the lifetime of the machine part is less than 6.
(A) 0.04
(B) 0.15
(C) 0.47
(D) 0.53
(E) 0.94
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35. This question duplicates Question 34 and has been deleted.
36. A group insurance policy covers the medical claims of the employees of a small
company. The value, V, of the claims made in one year is described by
V = 100,000Y
where Y is a random variable with density function
4
(1 ) , 0 1
( )
0, otherwise
k y y
f y
   
 

where k is a constant.
Calculate the conditional probability that V exceeds 40,000, given that V exceeds 10,000.
(A) 0.08
(B) 0.13
(C) 0.17
(D) 0.20
(E) 0.51
37. The lifetime of a printer costing 200 is exponentially distributed with mean 2 years. The
manufacturer agrees to pay a full refund to a buyer if the printer fails during the first year
following its purchase, a one-half refund if it fails during the second year, and no refund
for failure after the second year.
Calculate the expected total amount of refunds from the sale of 100 printers.
(A) 6,321
(B) 7,358
(C) 7,869
(D) 10,256
(E) 12,642
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38. An insurance company insures a large number of homes. The insured value, X, of a
randomly selected home is assumed to follow a distribution with density function
4
3 , 1
( )
0, otherwise.
x x
f x

 
 

Given that a randomly selected home is insured for at least 1.5, calculate the probability
that it is insured for less than 2.
(A) 0.578
(B) 0.684
(C) 0.704
(D) 0.829
(E) 0.875
39. A company prices its hurricane insurance using the following assumptions:
(i) In any calendar year, there can be at most one hurricane.
(ii) In any calendar year, the probability of a hurricane is 0.05.
(iii) The numbers of hurricanes in different calendar years are mutually
independent.
Using the company’s assumptions, calculate the probability that there are fewer
than 3 hurricanes in a 20-year period.
(A) 0.06
(B) 0.19
(C) 0.38
(D) 0.62
(E) 0.92
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40. An insurance policy pays for a random loss X subject to a deductible of C, where
0 1C  . The loss amount is modeled as a continuous random variable with density
function
2 , 0 1
( )
0, otherwise.
x x
f x
 
 

Given a random loss X, the probability that the insurance payment is less than 0.5 is equal
to 0.64.
Calculate C.
(A) 0.1
(B) 0.3
(C) 0.4
(D) 0.6
(E) 0.8
41. A study is being conducted in which the health of two independent groups of ten
policyholders is being monitored over a one-year period of time. Individual participants
in the study drop out before the end of the study with probability 0.2 (independently of
the other participants).
Calculate the probability that at least nine participants complete the study in one of the
two groups, but not in both groups?
(A) 0.096
(B) 0.192
(C) 0.235
(D) 0.376
(E) 0.469
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42. For Company A there is a 60% chance that no claim is made during the coming year. If
one or more claims are made, the total claim amount is normally distributed with mean
10,000 and standard deviation 2,000.
For Company B there is a 70% chance that no claim is made during the coming year. If
one or more claims are made, the total claim amount is normally distributed with mean
9,000 and standard deviation 2,000.
The total claim amounts of the two companies are independent.
Calculate the probability that, in the coming year, Company B’s total claim amount will
exceed Company A’s total claim amount.
(A) 0.180
(B) 0.185
(C) 0.217
(D) 0.223
(E) 0.240
43. A company takes out an insurance policy to cover accidents that occur at its
manufacturing plant. The probability that one or more accidents will occur during any
given month is 0.60. The numbers of accidents that occur in different months are
mutually independent.
Calculate the probability that there will be at least four months in which no accidents
occur before the fourth month in which at least one accident occurs.
(A) 0.01
(B) 0.12
(C) 0.23
(D) 0.29
(E) 0.41
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44. An insurance policy pays 100 per day for up to three days of hospitalization and 50 per
day for each day of hospitalization thereafter.
The number of days of hospitalization, X, is a discrete random variable with probability
function
6
, 1,2,3,4,5
[ ] 15
0, otherwise.
k
k
P X k


  

Determine the expected payment for hospitalization under this policy.
(A) 123
(B) 210
(C) 220
(D) 270
(E) 367
45. Let X be a continuous random variable with density function
| |
, 2 4
( ) 10
0, otherwise.
x
x
f x

  
 

Calculate the expected value of X.
(A) 1/5
(B) 3/5
(C) 1
(D) 28/15
(E) 12/5
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46. A device that continuously measures and records seismic activity is placed in a remote
region. The time, T, to failure of this device is exponentially distributed with mean 3
years. Since the device will not be monitored during its first two years of service, the
time to discovery of its failure is X = max(T, 2).
Calculate E(X).
(A) 61
2
3
e

(B) 2/3 4/3
2 2 5e e 
 
(C) 3
(D) 2/3
2 3e

(E) 5
47. A piece of equipment is being insured against early failure. The time from purchase until
failure of the equipment is exponentially distributed with mean 10 years. The insurance
will pay an amount x if the equipment fails during the first year, and it will pay 0.5x if
failure occurs during the second or third year. If failure occurs after the first three years,
no payment will be made.
Calculate x such that the expected payment made under this insurance is 1000.
(A) 3858
(B) 4449
(C) 5382
(D) 5644
(E) 7235
48. An insurance policy on an electrical device pays a benefit of 4000 if the device fails
during the first year. The amount of the benefit decreases by 1000 each successive year
until it reaches 0. If the device has not failed by the beginning of any given year, the
probability of failure during that year is 0.4.
Calculate the expected benefit under this policy.
(A) 2234
(B) 2400
(C) 2500
(D) 2667
(E) 2694
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49. This question duplicates Question 44 and has been deleted
50. A company buys a policy to insure its revenue in the event of major snowstorms that shut
down business. The policy pays nothing for the first such snowstorm of the year and
10,000 for each one thereafter, until the end of the year. The number of major
snowstorms per year that shut down business is assumed to have a Poisson distribution
with mean 1.5.
Calculate the expected amount paid to the company under this policy during a one-year
period.
(A) 2,769
(B) 5,000
(C) 7,231
(D) 8,347
(E) 10,578
51. A manufacturer’s annual losses follow a distribution with density function
2.5
3.5
2.5(0.6)
, 0.6
( )
0, otherwise.
x
f x x


 


To cover its losses, the manufacturer purchases an insurance policy with an annual
deductible of 2.
Calculate the mean of the manufacturer’s annual losses not paid by the insurance policy.
(A) 0.84
(B) 0.88
(C) 0.93
(D) 0.95
(E) 1.00
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52. An insurance company sells a one-year automobile policy with a deductible of 2. The
probability that the insured will incur a loss is 0.05. If there is a loss, the probability of a
loss of amount N is K/N, for N = 1, . . . , 5 and K a constant. These are the only possible
loss amounts and no more than one loss can occur.
Calculate the expected payment for this policy.
(A) 0.031
(B) 0.066
(C) 0.072
(D) 0.110
(E) 0.150
53. An insurance policy reimburses a loss up to a benefit limit of 10. The policyholder’s
loss, Y, follows a distribution with density function:
3
2 , 1
( )
0, otherwise.
y y
f y

 
 

Calculate the expected value of the benefit paid under the insurance policy.
(A) 1.0
(B) 1.3
(C) 1.8
(D) 1.9
(E) 2.0
54. An auto insurance company insures an automobile worth 15,000 for one year under a
policy with a 1,000 deductible. During the policy year there is a 0.04 chance of partial
damage to the car and a 0.02 chance of a total loss of the car. If there is partial damage to
the car, the amount X of damage (in thousands) follows a distribution with density
function
/2
0.5003 , 0 15
( )
0, otherwise.
x
e x
f x

  
 

Calculate the expected claim payment.
(A) 320
(B) 328
(C) 352
(D) 380
(E) 540
55. An insurance company’s monthly claims are modeled by a continuous, positive random
variable X, whose probability density function is proportional to (1 + x)4
, for 0 x   .
Calculate the company’s expected monthly claims.
Page 24 of 137
(A) 1/6
(B) 1/3
(C) 1/2
(D) 1
(E) 3
56. An insurance policy is written to cover a loss, X, where X has a uniform distribution on
[0, 1000]. The policy has a deductible, d, and the expected payment under the policy is
25% of what it would be with no deductible.
Calculate d.
(A) 250
(B) 375
(C) 500
(D) 625
(E) 750
57. An actuary determines that the claim size for a certain class of accidents is a random
variable, X, with moment generating function
4
1
( )
(1 2500 )
XM t
t


.
Calculate the standard deviation of the claim size for this class of accidents.
(A) 1,340
(B) 5,000
(C) 8,660
(D) 10,000
(E) 11,180
Page 25 of 137
58. A company insures homes in three cities, J, K, and L. Since sufficient distance separates
the cities, it is reasonable to assume that the losses occurring in these cities are mutually
independent.
The moment generating functions for the loss distributions of the cities are:
3 2.5 4.5
( ) (1 2 ) , ( ) (1 2 ) , ( ) (1 2 ) .J K LM t t M t t M t t  
     
Let X represent the combined losses from the three cities.
Calculate 3
( )E X .
(A) 1,320
(B) 2,082
(C) 5,760
(D) 8,000
(E) 10,560
59. An insurer's annual weather-related loss, X, is a random variable with density function
2.5
3.5
2.5(200)
, 200
( )
0, otherwise.
x
f x x


 


Calculate the difference between the 30th
and 70th
percentiles of X.
(A) 35
(B) 93
(C) 124
(D) 231
(E) 298
Page 26 of 137
60. A recent study indicates that the annual cost of maintaining and repairing a car in a town
in Ontario averages 200 with a variance of 260.
A tax of 20% is introduced on all items associated with the maintenance and repair of
cars (i.e., everything is made 20% more expensive).
Calculate the variance of the annual cost of maintaining and repairing a car after the tax is
introduced.
(A) 208
(B) 260
(C) 270
(D) 312
(E) 374
61. This question duplicates Question 59 and has been deleted
62. A random variable X has the cumulative distribution function
2
0, 1
2 2
( ) , 1 2
2
1, 2.
x
x x
F x x
x


 
  


Calculate the variance of X.
(A) 7/72
(B) 1/8
(C) 5/36
(D) 4/3
(E) 23/12
Page 27 of 137
63. The warranty on a machine specifies that it will be replaced at failure or age 4, whichever
occurs first. The machine’s age at failure, X, has density function
1/ 5, 0 5
( )
0, otherwise.
x
f x
 
 

Let Y be the age of the machine at the time of replacement.
Calculate the variance of Y.
(A) 1.3
(B) 1.4
(C) 1.7
(D) 2.1
(E) 7.5
64. A probability distribution of the claim sizes for an auto insurance policy is given in the
table below:
Claim
Size Probability
20
30
40
50
60
70
80
0.15
0.10
0.05
0.20
0.10
0.10
0.30
Calculate the percentage of claims that are within one standard deviation of the mean
claim size.
(A) 45%
(B) 55%
(C) 68%
(D) 85%
(E) 100%
65. The owner of an automobile insures it against damage by purchasing an insurance policy
with a deductible of 250. In the event that the automobile is damaged, repair costs can be
modeled by a uniform random variable on the interval (0, 1500).
Calculate the standard deviation of the insurance payment in the event that the
automobile is damaged.
(A) 361
(B) 403
Page 28 of 137
(C) 433
(D) 464
(E) 521
66. DELETED
67. A baseball team has scheduled its opening game for April 1. If it rains on April 1, the
game is postponed and will be played on the next day that it does not rain. The team
purchases insurance against rain. The policy will pay 1000 for each day, up to 2 days,
that the opening game is postponed.
The insurance company determines that the number of consecutive days of rain beginning
on April 1 is a Poisson random variable with mean 0.6.
Calculate the standard deviation of the amount the insurance company will have to pay.
(A) 668
(B) 699
(C) 775
(D) 817
(E) 904
Page 29 of 137
68. An insurance policy reimburses dental expense, X, up to a maximum benefit of 250. The
probability density function for X is:
0.004
, 0
( )
0, otherwise
x
ce x
f x

 
 

where c is a constant.
Calculate the median benefit for this policy.
(A) 161
(B) 165
(C) 173
(D) 182
(E) 250
69. The time to failure of a component in an electronic device has an exponential distribution
with a median of four hours.
Calculate the probability that the component will work without failing for at least five
hours.
(A) 0.07
(B) 0.29
(C) 0.38
(D) 0.42
(E) 0.57
70. An insurance company sells an auto insurance policy that covers losses incurred by a
policyholder, subject to a deductible of 100. Losses incurred follow an exponential
distribution with mean 300.
Calculate the 95th
percentile of losses that exceed the deductible.
(A) 600
(B) 700
(C) 800
(D) 900
(E) 1000
Page 30 of 137
71. The time, T, that a manufacturing system is out of operation has cumulative distribution
function
2
2
1 , 2
( )
0, otherwise.
t
F t t
  
      


The resulting cost to the company is 2
Y T . Let g be the density function for Y.
Determine g(y), for y > 4.
(A) 2
4
y
(B) 3/2
8
y
(C) 3
8
y
(D)
16
y
(E) 5
1024
y
72. An investment account earns an annual interest rate R that follows a uniform distribution
on the interval (0.04, 0.08). The value of a 10,000 initial investment in this account after
one year is given by 10,000 R
V e .
Let F be the cumulative distribution function of V.
Determine F(v) for values of v that satisfy 0 ( ) 1F v  .
(A)
/10,000
10,000 10,408
425
v
e 
(B) /10,000
25 0.04v
e 
(C)
10,408
10,833 10,408
v 

(D)
25
v
(E) 25 ln 0.04
10,000
v  
  
  
Page 31 of 137
73. An actuary models the lifetime of a device using the random variable Y = 10X 0.8
, where
X is an exponential random variable with mean 1.
Let f(y) be the density function for Y.
Determine f (y), for y > 0.
(A) 0.8 0.2
10 exp( 8 )y y

(B) 0.2 0.8
8 exp( 10 )y y

(C) 0.2 1.25
8 exp[ (0.1 ) ]y y

(D) 1.25 0.25
(0.1 ) exp[ 0.125(0.1 ) ]y y
(E)
0.25 1.25
0.125(0.1 ) exp[ (0.1 ) ]y y
74. Let T denote the time in minutes for a customer service representative to respond to 10
telephone inquiries. T is uniformly distributed on the interval with endpoints 8 minutes
and 12 minutes.
Let R denote the average rate, in customers per minute, at which the representative
responds to inquiries, and let f(r) be the density function for R.
Determine f(r), for
10 10
12 8
r  .
(A)
12
5
(B)
5
3
2r

(C)
5ln( )
3
2
r
r 
(D) 2
10
r
(E) 2
5
2r
Page 32 of 137
75. The monthly profit of Company I can be modeled by a continuous random variable with
density function f. Company II has a monthly profit that is twice that of Company I.
Let g be the density function for the distribution of the monthly profit of Company II.
Determine g(y) where it is not zero.
(A)
1
2 2
y
f
 
 
 
(B)
2
y
f
 
 
 
(C) 2
2
y
f
 
 
 
(D) 2 ( )f y
(E) 2 (2 )f y
76. Claim amounts for wind damage to insured homes are mutually independent random
variables with common density function
4
3
, 1
( )
0, otherwise,
x
f x x


 

where x is the amount of a claim in thousands.
Suppose 3 such claims will be made.
Calculate the expected value of the largest of the three claims.
(A) 2025
(B) 2700
(C) 3232
(D) 3375
(E) 4500
Page 33 of 137
77. A device runs until either of two components fails, at which point the device stops
running. The joint density function of the lifetimes of the two components, both
measured in hours, is
( , ) , for 0 2 and 0 2
8
x y
f x y x y

     .
Calculate the probability that the device fails during its first hour of operation.
(A) 0.125
(B) 0.141
(C) 0.391
(D) 0.625
(E) 0.875
78. This question duplicates Question 77 and has been deleted
79. A device contains two components. The device fails if either component fails. The joint
density function of the lifetimes of the components, measured in hours, is f(s,t), where 0
< s < 1 and 0 < t < 1.
Determine which of the following represents the probability that the device fails during
the first half hour of operation.
(A)
0.5 0.5
0 0
( , )f s t dsdt 
(B)
1 0.5
0 0
( , )f s t dsdt 
(C)
1 1
0.5 0.5
( , )f s t dsdt 
(D)
0.5 1 1 0.5
0 0 0 0
( , ) ( , )f s t dsdt f s t dsdt   
(E)
0.5 1 1 0.5
0 0.5 0 0
( , ) ( , )f s t dsdt f s t dsdt   
Page 34 of 137
80. A charity receives 2025 contributions. Contributions are assumed to be mutually
independent and identically distributed with mean 3125 and standard deviation 250.
Calculate the approximate 90th
percentile for the distribution of the total contributions
received.
(A) 6,328,000
(B) 6,338,000
(C) 6,343,000
(D) 6,784,000
(E) 6,977,000
81. Claims filed under auto insurance policies follow a normal distribution with mean 19,400
and standard deviation 5,000.
Calculate the probability that the average of 25 randomly selected claims exceeds 20,000.
(A) 0.01
(B) 0.15
(C) 0.27
(D) 0.33
(E) 0.45
82. An insurance company issues 1250 vision care insurance policies. The number of claims
filed by a policyholder under a vision care insurance policy during one year is a Poisson
random variable with mean 2. Assume the numbers of claims filed by different
policyholders are mutually independent.
Calculate the approximate probability that there is a total of between 2450 and 2600
claims during a one-year period?
(A) 0.68
(B) 0.82
(C) 0.87
(D) 0.95
(E) 1.00
Page 35 of 137
83. A company manufactures a brand of light bulb with a lifetime in months that is normally
distributed with mean 3 and variance 1. A consumer buys a number of these bulbs with
the intention of replacing them successively as they burn out. The light bulbs have
mutually independent lifetimes.
Calculate the smallest number of bulbs to be purchased so that the succession of light
bulbs produces light for at least 40 months with probability at least 0.9772.
(A) 14
(B) 16
(C) 20
(D) 40
(E) 55
84. Let X and Y be the number of hours that a randomly selected person watches movies and
sporting events, respectively, during a three-month period. The following information is
known about X and Y:
E(X) = 50, E(Y) = 20, Var(X) = 50, Var(Y) = 30, Cov(X,Y) = 10.
The totals of hours that different individuals watch movies and sporting events during the
three months are mutually independent.
One hundred people are randomly selected and observed for these three months. Let T be
the total number of hours that these one hundred people watch movies or sporting events
during this three-month period.
Approximate the value of P[T < 7100].
(A) 0.62
(B) 0.84
(C) 0.87
(D) 0.92
(E) 0.97
Page 36 of 137
85. The total claim amount for a health insurance policy follows a distribution with density
function
( /1000)1
( )
1000
x
f x e
 , x > 0.
The premium for the policy is set at the expected total claim amount plus 100.
If 100 policies are sold, calculate the approximate probability that the insurance company
will have claims exceeding the premiums collected.
(A) 0.001
(B) 0.159
(C) 0.333
(D) 0.407
(E) 0.460
86. A city has just added 100 new female recruits to its police force. The city will provide a
pension to each new hire who remains with the force until retirement. In addition, if the
new hire is married at the time of her retirement, a second pension will be provided for
her husband. A consulting actuary makes the following assumptions:
(i) Each new recruit has a 0.4 probability of remaining with
the police force until retirement.
(ii) Given that a new recruit reaches retirement with the police
force, the probability that she is not married at the time of
retirement is 0.25.
(iii) The events of different new hires reaching retirement and the
events of different new hires being married at retirement are all
mutually independent events.
Calculate the probability that the city will provide at most 90 pensions to the 100 new
hires and their husbands.
(A) 0.60
(B) 0.67
(C) 0.75
(D) 0.93
(E) 0.99
Page 37 of 137
87. In an analysis of healthcare data, ages have been rounded to the nearest multiple of 5
years. The difference between the true age and the rounded age is assumed to be
uniformly distributed on the interval from 2.5 years to 2.5 years. The healthcare data
are based on a random sample of 48 people.
Calculate the approximate probability that the mean of the rounded ages is within 0.25
years of the mean of the true ages.
(A) 0.14
(B) 0.38
(C) 0.57
(D) 0.77
(E) 0.88
88. The waiting time for the first claim from a good driver and the waiting time for the first
claim from a bad driver are independent and follow exponential distributions with means
6 years and 3 years, respectively.
Calculate the probability that the first claim from a good driver will be filed within
3 years and the first claim from a bad driver will be filed within 2 years.
(A)  2/3 1/2 7/61
1
18
e e e  
  
(B) 7/61
18
e
(C) 2/3 1/2 7/6
1 e e e  
  
(D) 2/3 1/2 1/3
1 e e e  
  
(E) 2/3 1/2 7/61 1 1
1
3 6 18
e e e  
  
Page 38 of 137
89. The future lifetimes (in months) of two components of a machine have the following joint
density function:
6
(50 ), 0 50 50
( , ) 125,000
0, otherwise.
x y x y
f x y

     
 


Determine which of the following represents the probability that both components are
still functioning 20 months from now.
(A)
20 20
0 0
6
(50 )
125,000
x y dydx  
(B)
30 50
20 20
6
(50 )
125,000
x
x y dydx

  
(C)
5030
20 20
6
(50 )
125,000
x y
x y dydx
 
  
(D)
50 50
20 20
6
(50 )
125,000
x
x y dydx

  
(E)
5050
20 20
6
(50 )
125,000
x y
x y dydx
 
  
90. An insurance company sells two types of auto insurance policies: Basic and Deluxe. The
time until the next Basic Policy claim is an exponential random variable with mean two
days. The time until the next Deluxe Policy claim is an independent exponential random
variable with mean three days.
Calculate the probability that the next claim will be a Deluxe Policy claim.
(A) 0.172
(B) 0.223
(C) 0.400
(D) 0.487
(E) 0.500
Page 39 of 137
91. An insurance company insures a large number of drivers. Let X be the random variable
representing the company’s losses under collision insurance, and let Y represent the
company’s losses under liability insurance. X and Y have joint density function
2 2
, 0 1 and 0 2
( , ) 4
0, otherwise.
x y
x y
f x y
 
   
 

Calculate the probability that the total company loss is at least 1.
(A) 0.33
(B) 0.38
(C) 0.41
(D) 0.71
(E) 0.75
92. Two insurers provide bids on an insurance policy to a large company. The bids must be
between 2000 and 2200. The company decides to accept the lower bid if the two bids
differ by 20 or more. Otherwise, the company will consider the two bids further.
Assume that the two bids are independent and are both uniformly distributed on the
interval from 2000 to 2200.
Calculate the probability that the company considers the two bids further.
(A) 0.10
(B) 0.19
(C) 0.20
(D) 0.41
(E) 0.60
93. A family buys two policies from the same insurance company. Losses under the two
policies are independent and have continuous uniform distributions on the interval from 0
to 10. One policy has a deductible of 1 and the other has a deductible of 2. The family
experiences exactly one loss under each policy.
Calculate the probability that the total benefit paid to the family does not exceed 5.
(A) 0.13
(B) 0.25
(C) 0.30
(D) 0.32
(E) 0.42
Page 40 of 137
94. Let T1 be the time between a car accident and reporting a claim to the insurance
company. Let T2 be the time between the report of the claim and payment of the claim.
The joint density function of T1 and T2, 1 2( , )f t t , is constant over the region
1 2 1 20 6, 0 6, 10t t t t      , and zero otherwise.
Calculate E(T1 + T2), the expected time between a car accident and payment of the claim.
(A) 4.9
(B) 5.0
(C) 5.7
(D) 6.0
(E) 6.7
95. X and Y are independent random variables with common moment generating function
2
( ) exp( / 2)M t t .
Let andW X Y Z Y X    .
Determine the joint moment generating function, 1 2( , )M t t of W and Z.
(A)
2 2
1 2exp(2 2 )t t
(B)
2
1 2exp[( ) ]t t
(C)
2
1 2exp[( ) ]t t
(D) 1 2exp(2 )t t
(E)
2 2
1 2exp( )t t
96. A tour operator has a bus that can accommodate 20 tourists. The operator knows that
tourists may not show up, so he sells 21 tickets. The probability that an individual tourist
will not show up is 0.02, independent of all other tourists.
Each ticket costs 50, and is non-refundable if a tourist fails to show up. If a tourist shows
up and a seat is not available, the tour operator has to pay 100 (ticket cost + 50 penalty) to
the tourist.
Calculate the expected revenue of the tour operator.
(A) 955
(B) 962
(C) 967
(D) 976
(E) 985
Page 41 of 137
97. Let T1 and T2 represent the lifetimes in hours of two linked components in an electronic
device. The joint density function for T1 and T2 is uniform over the region defined by
1 20 t t L   where L is a positive constant.
Determine the expected value of the sum of the squares of T1 and T2.
(A)
2
3
L
(B)
2
2
L
(C)
2
2
3
L
(D)
2
3
4
L
(E) 2
L
98. Let X1, X2, X3 be a random sample from a discrete distribution with probability function
1/ 3, 0
( ) 2 / 3, 1
0, otherwise.
x
p x x


 


Determine the moment generating function, M(t), of Y = X1X2X3 .
(A)
19 8
27 27
t
e
(B) 1 2 t
e
(C)
3
1 2
3 3
t
e
 
 
 
(D) 31 8
27 27
t
e
(E) 31 2
3 3
t
e
Page 42 of 137
99. An insurance policy pays a total medical benefit consisting of two parts for each claim.
Let X represent the part of the benefit that is paid to the surgeon, and let Y represent the
part that is paid to the hospital. The variance of X is 5000, the variance of Y is 10,000,
and the variance of the total benefit, X + Y, is 17,000.
Due to increasing medical costs, the company that issues the policy decides to increase X
by a flat amount of 100 per claim and to increase Y by 10% per claim.
Calculate the variance of the total benefit after these revisions have been made.
(A) 18,200
(B) 18,800
(C) 19,300
(D) 19,520
(E) 20,670
100. A car dealership sells 0, 1, or 2 luxury cars on any day. When selling a car, the dealer
also tries to persuade the customer to buy an extended warranty for the car. Let X denote
the number of luxury cars sold in a given day, and let Y denote the number of extended
warranties sold.
P[X = 0, Y = 0] = 1/6
P[X = 1, Y = 0] = 1/12
P[X = 1, Y = 1] = 1/6
P[X = 2, Y = 0] = 1/12
P[X = 2, Y = 1] = 1/3
P[X = 2, Y = 2] = 1/6
Calculate the variance of X.
(A) 0.47
(B) 0.58
(C) 0.83
(D) 1.42
(E) 2.58
Page 43 of 137
101. The profit for a new product is given by Z = 3X – Y  5. X and Y are independent random
variables with Var(X) = 1 and Var(Y) = 2.
Calculate Var(Z).
(A) 1
(B) 5
(C) 7
(D) 11
(E) 16
102. A company has two electric generators. The time until failure for each generator follows
an exponential distribution with mean 10. The company will begin using the second
generator immediately after the first one fails.
Calculate the variance of the total time that the generators produce electricity.
(A) 10
(B) 20
(C) 50
(D) 100
(E) 200
103. In a small metropolitan area, annual losses due to storm, fire, and theft are assumed to be
mutually independent, exponentially distributed random variables with respective means
1.0, 1.5, and 2.4.
Calculate the probability that the maximum of these losses exceeds 3.
(A) 0.002
(B) 0.050
(C) 0.159
(D) 0.287
(E) 0.414
104. A joint density function is given by
 
, 0 1, 0 1
,
0, otherwise.
kx x y
f x y
   
 

where k is a constant.
Calculate Cov(X,Y).
(A) –1/6
(B) 0
Page 44 of 137
(C) 1/9
(D) 1/6
(E) 2/3
105. Let X and Y be continuous random variables with joint density function
8
, 0 1, 2
( , ) 3
0, otherwise.
xy x x y x
f x y

   
 

Calculate the covariance of X and Y.
(A) 0.04
(B) 0.25
(C) 0.67
(D) 0.80
(E) 1.24
106. Let X and Y denote the values of two stocks at the end of a five-year period. X is
uniformly distributed on the interval (0, 12). Given X = x, Y is uniformly distributed on
the interval (0, x).
Calculate Cov(X, Y) according to this model.
(A) 0
(B) 4
(C) 6
(D) 12
(E) 24
Page 45 of 137
107. Let X denote the size of a surgical claim and let Y denote the size of the associated
hospital claim. An actuary is using a model in which
2 2
( ) 5, ( ) 27.4, ( ) 7, ( ) 51.4, ( ) 8.E X E X E Y E Y Var X Y     
Let 1C X Y  denote the size of the combined claims before the application of a 20%
surcharge on the hospital portion of the claim, and let 2C denote the size of the combined
claims after the application of that surcharge.
Calculate 1 2( , )Cov C C .
(A) 8.80
(B) 9.60
(C) 9.76
(D) 11.52
(E) 12.32
108. A device containing two key components fails when, and only when, both components
fail. The lifetimes, 1T and 2T of these components are independent with common density
function
, 0
( )
0, otherwise.
t
e t
f t

 
 

The cost, X, of operating the device until failure is 1 22T T . Let g be the density function
for X.
Determine g(x), for x > 0.
(A) /2x x
e e 

(B)  /2
2 x x
e e 

(C)
2
2
x
x e
(D)
/2
2
x
e
(E)
/3
3
x
e
109. A company offers earthquake insurance. Annual premiums are modeled by an
exponential random variable with mean 2. Annual claims are modeled by an exponential
random variable with mean 1. Premiums and claims are independent. Let X denote the
ratio of claims to premiums, and let f be the density function of X.
Page 46 of 137
Determine f(x), where it is positive.
(A)
1
2 1x 
(B) 2
2
(2 1)x 
(C) x
e
(D) 2
2 x
e
(E) x
xe
110. Let X and Y be continuous random variables with joint density function
 
24 , 0 1, 0 1
,
0, otherwise.
xy x y x
f x y
    
 

Calculate
1
P
3
Y X X
 
   
.
(A) 1/27
(B) 2/27
(C) 1/4
(D) 1/3
(E) 4/9
Page 47 of 137
111. Once a fire is reported to a fire insurance company, the company makes an initial
estimate, X, of the amount it will pay to the claimant for the fire loss. When the claim is
finally settled, the company pays an amount, Y, to the claimant. The company has
determined that X and Y have the joint density function
 
(2 1) ( 1)
2
2
, 1, 1
( 1),
0, otherwise.
x x
y x y
x xf x y
  
 
 


Given that the initial claim estimated by the company is 2, calculate the probability that
the final settlement amount is between 1 and 3.
(A) 1/9
(B) 2/9
(C) 1/3
(D) 2/3
(E) 8/9
112. A company offers a basic life insurance policy to its employees, as well as a
supplemental life insurance policy. To purchase the supplemental policy, an employee
must first purchase the basic policy.
Let X denote the proportion of employees who purchase the basic policy, and Y the
proportion of employees who purchase the supplemental policy. Let X and Y have the
joint density function f(x,y) = 2(x + y) on the region where the density is positive.
Given that 10% of the employees buy the basic policy, calculate the probability that
fewer than 5% buy the supplemental policy.
(A) 0.010
(B) 0.013
(C) 0.108
(D) 0.417
(E) 0.500
113. Two life insurance policies, each with a death benefit of 10,000 and a one-time premium
of 500, are sold to a married couple, one for each person. The policies will expire at the
end of the tenth year. The probability that only the wife will survive at least ten years is
0.025, the probability that only the husband will survive at least ten years is 0.01, and the
probability that both of them will survive at least ten years is 0.96.
Calculate the expected excess of premiums over claims, given that the husband survives
at least ten years.
(A) 350
Page 48 of 137
(B) 385
(C) 397
(D) 870
(E) 897
114. A diagnostic test for the presence of a disease has two possible outcomes: 1 for disease
present and 0 for disease not present. Let X denote the disease state (0 or 1) of a patient,
and let Y denote the outcome of the diagnostic test. The joint probability function of X
and Y is given by:
P[X = 0, Y = 0] = 0.800
P[X = 1, Y = 0] = 0.050
P[X = 0, Y = 1] = 0.025
P[X = 1, Y = 1] = 0.125
Calculate Var( 1)Y X  .
(A) 0.13
(B) 0.15
(C) 0.20
(D) 0.51
(E) 0.71
Page 49 of 137
115. The stock prices of two companies at the end of any given year are modeled with random
variables X and Y that follow a distribution with joint density function
2 , 0 1, 1
( , )
0, otherwise.
x x x y x
f x y
    
 

Determine the conditional variance of Y given that X = x.
(A) 1/12
(B) 7/6
(C) x + 1/2
(D) 2
1/ 6x 
(E) 2
1/ 3x x 
116. An actuary determines that the annual number of tornadoes in counties P and Q are
jointly distributed as follows:
Annual number of
tornadoes in county Q
0 1 2 3
Annual number 0 0.12 0.06 0.05 0.02
of tornadoes 1 0.13 0.15 0.12 0.03
in county P 2 0.05 0.15 0.10 0.02
Calculate the conditional variance of the annual number of tornadoes in county Q, given
that there are no tornadoes in county P.
(A) 0.51
(B) 0.84
(C) 0.88
(D) 0.99
(E) 1.76
117. A company is reviewing tornado damage claims under a farm insurance policy. Let X be
the portion of a claim representing damage to the house and let Y be the portion of the
same claim representing damage to the rest of the property. The joint density function of
X and Y is
 6 1 ( ) , 0, 0, 1
( , )
0, otherwise.
x y x y x y
f x y
      
 

Calculate the probability that the portion of a claim representing damage to the house is
less than 0.2.
(A) 0.360
Page 50 of 137
(B) 0.480
(C) 0.488
(D) 0.512
(E) 0.520
118. Let X and Y be continuous random variables with joint density function
 
2
15 ,
,
0, otherwise.
y x y x
f x y
  
 

Let g be the marginal density function of Y.
Determine which of the following represents g.
(A)  
15 , 0 1
0, otherwise
y y
g y
 
 

(B)  
2
215
,
2
0, otherwise
y
x y x
g y

 
 


(C)  
2
15
, 0 1
2
0, otherwise
y
y
g y

 
 


(D)  
3/2 1/2 2
15 (1 ),
0, otherwise
y y x y x
g y
   
 

(E)  
3/2 1/2
15 (1 ), 0 1
0, otherwise
y y y
g y
   
 

119. An auto insurance policy will pay for damage to both the policyholder’s car and the other
driver’s car in the event that the policyholder is responsible for an accident. The size of
the payment for damage to the policyholder’s car, X, has a marginal density function of 1
for 0 < x < 1. Given X = x, the size of the payment for damage to the other driver’s car,
Y, has conditional density of 1 for x < y < x + 1.
Given that the policyholder is responsible for an accident, calculate the probability that
the payment for damage to the other driver’s car will be greater than 0.5.
(A) 3/8
(B) 1/2
(C) 3/4
(D) 7/8
(E) 15/16
Page 51 of 137
120. An insurance policy is written to cover a loss X where X has density function
23
, 0 2
( ) 8
0, otherwise.
x x
f x

 
 

The time (in hours) to process a claim of size x, where 0 2x  , is uniformly distributed
on the interval from x to 2x.
Calculate the probability that a randomly chosen claim on this policy is processed in three
hours or more.
(A) 0.17
(B) 0.25
(C) 0.32
(D) 0.58
(E) 0.83
Page 52 of 137
121. Let X represent the age of an insured automobile involved in an accident. Let Y represent
the length of time the owner has insured the automobile at the time of the accident.
X and Y have joint probability density function
 21
10 , 2 10, 0 1
64( , )
0, otherwise.
xy x y
f x y
     
 

Calculate the expected age of an insured automobile involved in an accident.
(A) 4.9
(B) 5.2
(C) 5.8
(D) 6.0
(E) 6.4
122. A device contains two circuits. The second circuit is a backup for the first, so the second
is used only when the first has failed. The device fails when and only when the second
circuit fails.
Let X and Y be the times at which the first and second circuits fail, respectively. X and Y
have joint probability density function
2
6e e , 0
( , )
0, otherwise.
x y
x y
f x y
 
    
 

Calculate the expected time at which the device fails.
(A) 0.33
(B) 0.50
(C) 0.67
(D) 0.83
(E) 1.50
Page 53 of 137
123. You are given the following information about N, the annual number of claims for a
randomly selected insured:
1 1 1
( 0) , ( 1) , ( 1)
2 3 6
P N P N P N      .
Let S denote the total annual claim amount for an insured. When N = 1, S is
exponentially distributed with mean 5. When N > 1, S is exponentially distributed with
mean 8.
Calculate P(4 < S < 8).
(A) 0.04
(B) 0.08
(C) 0.12
(D) 0.24
(E) 0.25
124. The joint probability density for X and Y is
( 2 )
2 , 0, 0
( , )
0, otherwise.
x y
e x y
f x y
 
  
 

Calculate the variance of Y given that X > 3 and Y > 3.
(A) 0.25
(B) 0.50
(C) 1.00
(D) 3.25
(E) 3.50
125. The distribution of ,Y given ,X is uniform on the interval [0, X]. The marginal density
of X is
2 , 0 1
( )
0, otherwise.
x x
f x
 
 

Determine the conditional density of ,X given Y = y where positive.
(A) 1
(B) 2
(C) 2x
(D) 1/y
(E) 1/(1 – y)
Page 54 of 137
126. Under an insurance policy, a maximum of five claims may be filed per year by a
policyholder. Let ( )p n be the probability that a policyholder files n claims during a given
year, where n = 0,1,2,3,4,5. An actuary makes the following observations:
i) ( ) ( 1) for 0, 1, 2, 3, 4p n p n n   .
ii) The difference between ( )p n and ( 1)p n  is the same for n = 0,1,2,3,4.
iii) Exactly 40% of policyholders file fewer than two claims during a given
year.
Calculate the probability that a random policyholder will file more than three claims
during a given year.
(A) 0.14
(B) 0.16
(C) 0.27
(D) 0.29
(E) 0.33
127. The amounts of automobile losses reported to an insurance company are mutually
independent, and each loss is uniformly distributed between 0 and 20,000. The company
covers each such loss subject to a deductible of 5,000.
Calculate the probability that the total payout on 200 reported losses is between
1,000,000 and 1,200,000.
(A) 0.0803
(B) 0.1051
(C) 0.1799
(D) 0.8201
(E) 0.8575
Page 55 of 137
128. An insurance agent offers his clients auto insurance, homeowners insurance and renters
insurance. The purchase of homeowners insurance and the purchase of renters insurance
are mutually exclusive. The profile of the agent’s clients is as follows:
i) 17% of the clients have none of these three products.
ii) 64% of the clients have auto insurance.
iii) Twice as many of the clients have homeowners insurance as have renters insurance.
iv) 35% of the clients have two of these three products.
v) 11% of the clients have homeowners insurance, but not auto insurance.
Calculate the percentage of the agent’s clients that have both auto and renters insurance.
(A) 7%
(B) 10%
(C) 16%
(D) 25%
(E) 28%
129. The cumulative distribution function for health care costs experienced by a policyholder
is modeled by the function
100
1 , 0( )
0, otherwise.
x
e xF x
   

The policy has a deductible of 20. An insurer reimburses the policyholder for 100% of
health care costs between 20 and 120 less the deductible. Health care costs above 120 are
reimbursed at 50%.
Let G be the cumulative distribution function of reimbursements given that the
reimbursement is positive.
Calculate G(115).
(A) 0.683
(B) 0.727
(C) 0.741
(D) 0.757
(E) 0.777
Page 56 of 137
130. The value of a piece of factory equipment after three years of use is 100(0.5)X
where X
is a random variable having moment generating function
1 1
( ) , .
1 2 2
XM t t
t
 

Calculate the expected value of this piece of equipment after three years of use.
(A) 12.5
(B) 25.0
(C) 41.9
(D) 70.7
(E) 83.8
131. Let 1N and 2N represent the numbers of claims submitted to a life insurance company in
April and May, respectively. The joint probability function of 1N and 2N is
 
1
2
1 1
1
1
1 2
1 2
3 1
1 , 1,2,3,..., 1,2,3,...
( , ) 4 4
0, otherwise.
n
n
n n
e e n n
p n n


 
  
       


Calculate the expected number of claims that will be submitted to the company in May,
given that exactly 2 claims were submitted in April.
(A)  23
1
16
e 
(B) 23
16
e
(C)
3
4
e
e
(D) 2
1e 
(E) 2
e
Page 57 of 137
132. A store has 80 modems in its inventory, 30 coming from Source A and the remainder
from Source B. Of the modems from Source A, 20% are defective. Of the modems from
Source B, 8% are defective.
Calculate the probability that exactly two out of a sample of five modems selected
without replacement from the store’s inventory are defective.
(A) 0.010
(B) 0.078
(C) 0.102
(D) 0.105
(E) 0.125
133. A man purchases a life insurance policy on his 40th
birthday. The policy will pay 5000 if
he dies before his 50th
birthday and will pay 0 otherwise. The length of lifetime, in years
from birth, of a male born the same year as the insured has the cumulative distribution
function
0, 0
( ) 1 1.1
1 exp , 0.
1000
t
t
F t
t


  
  
 
Calculate the expected payment under this policy.
(A) 333
(B) 348
(C) 421
(D) 549
(E) 574
134. A mattress store sells only king, queen and twin-size mattresses. Sales records at the
store indicate that the number of queen-size mattresses sold is one-fourth the number of
king and twin-size mattresses combined. Records also indicate that three times as many
king-size mattresses are sold as twin-size mattresses.
Calculate the probability that the next mattress sold is either king or queen-size.
(A) 0.12
(B) 0.15
(C) 0.80
(D) 0.85
(E) 0.95
Page 58 of 137
135. The number of workplace injuries, N, occurring in a factory on any given day is Poisson
distributed with mean λ. The parameter λ is a random variable that is determined by the
level of activity in the factory, and is uniformly distributed on the interval [0, 3].
Calculate Var(N).
(A) λ
(B) 2 λ
(C) 0.75
(D) 1.50
(E) 2.25
136. A fair die is rolled repeatedly. Let X be the number of rolls needed to obtain a 5 and Y
the number of rolls needed to obtain a 6.
Calculate E(X | Y = 2).
(A) 5.0
(B) 5.2
(C) 6.0
(D) 6.6
(E) 6.8
137. Let X and Y be identically distributed independent random variables such that the
moment generating function of X + Y is
2 2
( ) 0.09 0.24 0.34 0.24 0.09 , .t t t t
M t e e e e t 
        
Calculate P[X  0].
(A) 0.33
(B) 0.34
(C) 0.50
(D) 0.67
(E) 0.70
Page 59 of 137
138. A machine consists of two components, whose lifetimes have the joint density function
1
, 0, 0, 10
( , ) 50
0, otherwise.
x y x y
f x y

   
 

The machine operates until both components fail.
Calculate the expected operational time of the machine.
(A) 1.7
(B) 2.5
(C) 3.3
(D) 5.0
(E) 6.7
139. A driver and a passenger are in a car accident. Each of them independently has
probability 0.3 of being hospitalized. When a hospitalization occurs, the loss is
uniformly distributed on [0, 1]. When two hospitalizations occur, the losses are
independent.
Calculate the expected number of people in the car who are hospitalized, given that the
total loss due to hospitalizations from the accident is less than 1.
(A) 0.510
(B) 0.534
(C) 0.600
(D) 0.628
(E) 0.800
140. Each time a hurricane arrives, a new home has a 0.4 probability of experiencing damage.
The occurrences of damage in different hurricanes are mutually independent.
Calculate the mode of the number of hurricanes it takes for the home to experience
damage from two hurricanes.
(A) 2
(B) 3
(C) 4
(D) 5
(E) 6
Page 60 of 137
141. Thirty items are arranged in a 6-by-5 array as shown.
A1 A2 A3 A4 A5
A6 A7 A8 A9 A10
A11 A12 A13 A14 A15
A16 A17 A18 A19 A20
A21 A22 A23 A24 A25
A26 A27 A28 A29 A30
Calculate the number of ways to form a set of three distinct items such that no two of the
selected items are in the same row or same column.
(A) 200
(B) 760
(C) 1200
(D) 4560
(E) 7200
142. An auto insurance company is implementing a new bonus system. In each month, if a
policyholder does not have an accident, he or she will receive a cash-back bonus of 5 from
the insurer.
Among the 1,000 policyholders of the auto insurance company, 400 are classified as lowrisk
drivers and 600 are classified as high-risk drivers.
In each month, the probability of zero accidents for high-risk drivers is 0.80 and the
probability of zero accidents for low-risk drivers is 0.90.
Calculate the expected bonus payment from the insurer to the 1000 policyholders in one
year.
(A) 48,000
(B) 50,400
(C) 51,000
(D) 54,000
(E) 60,000
Page 61 of 137
143. The probability that a member of a certain class of homeowners with liability and
property coverage will file a liability claim is 0.04, and the probability that a member of
this class will file a property claim is 0.10. The probability that a member of this class
will file a liability claim but not a property claim is 0.01.
Calculate the probability that a randomly selected member of this class of homeowners
will not file a claim of either type.
(A) 0.850
(B) 0.860
(C) 0.864
(D) 0.870
(E) 0.890
144. A client spends X minutes in an insurance agent’s waiting room and Y minutes meeting
with the agent. The joint density function of X and Y can be modeled by
40 20
1
, 0, 0
( , ) 800
0, otherwise.
x y
e e x y
f x y
 
 
 


Determine which of the following expressions represents the probability that a client
spends less than 60 minutes at the agent’s office.
(A)
40 20
40 20
0 0
1
800
x y
e e dydx
 
 
(B)
40 20
40 20
0 0
1
800
x x y
e e dydx

 
 
(C)
20 40
40 20
0 0
1
800
x x y
e e dydx

 
 
(D)
60 60
40 20
0 0
1
800
x y
e e dydx
 
 
(E)
60 60
40 20
0 0
1
800
x x y
e e dydx

 
 
Page 62 of 137
145. New dental and medical plan options will be offered to state employees next year. An
actuary uses the following density function to model the joint distribution of the
proportion X of state employees who will choose Dental Option 1 and the proportion Y
who will choose Medical Option 1 under the new plan options:
0.50, 0 0.5, 0 0.5
1.25, 0 0.5, 0.5 1
( , )
1.50, 0.5 1, 0 0.5
0.75, 0.5 1, 0.5 1.
x y
x y
f x y
x y
x y
   
    
 
   
    
Calculate Var (Y | X = 0.75).
(A) 0.000
(B) 0.061
(C) 0.076
(D) 0.083
(E) 0.141
146. A survey of 100 TV viewers revealed that over the last year:
i) 34 watched CBS.
ii) 15 watched NBC.
iii) 10 watched ABC.
iv) 7 watched CBS and NBC.
v) 6 watched CBS and ABC.
vi) 5 watched NBC and ABC.
vii) 4 watched CBS, NBC, and ABC.
viii) 18 watched HGTV, and of these, none watched CBS, NBC, or ABC.
Calculate how many of the 100 TV viewers did not watch any of the four channels (CBS,
NBC, ABC or HGTV).
(A) 1
(B) 37
(C) 45
(D) 55
(E) 82
Page 63 of 137
147. The amount of a claim that a car insurance company pays out follows an exponential
distribution. By imposing a deductible of d, the insurance company reduces the expected
claim payment by 10%.
Calculate the percentage reduction on the variance of the claim payment.
(A) 1%
(B) 5%
(C) 10%
(D) 20%
(E) 25%
148. The number of hurricanes that will hit a certain house in the next ten years is Poisson
distributed with mean 4.
Each hurricane results in a loss that is exponentially distributed with mean 1000. Losses
are mutually independent and independent of the number of hurricanes.
Calculate the variance of the total loss due to hurricanes hitting this house in the next ten
years.
(A) 4,000,000
(B) 4,004,000
(C) 8,000,000
(D) 16,000,000
(E) 20,000,000
149. A motorist makes three driving errors, each independently resulting in an accident with
probability 0.25.
Each accident results in a loss that is exponentially distributed with mean 0.80. Losses
are mutually independent and independent of the number of accidents.
The motorist’s insurer reimburses 70% of each loss due to an accident.
Calculate the variance of the total unreimbursed loss the motorist experiences due to
accidents resulting from these driving errors.
(A) 0.0432
(B) 0.0756
(C) 0.1782
(D) 0.2520
(E) 0.4116
Page 64 of 137
150. An automobile insurance company issues a one-year policy with a deductible of 500.
The probability is 0.8 that the insured automobile has no accident and 0.0 that the
automobile has more than one accident. If there is an accident, the loss before
application of the deductible is exponentially distributed with mean 3000.
Calculate the 95th
percentile of the insurance company payout on this policy.
(A) 3466
(B) 3659
(C) 4159
(D) 8487
(E) 8987
151. From 27 pieces of luggage, an airline luggage handler damages a random sample of four.
The probability that exactly one of the damaged pieces of luggage is insured is twice the
probability that none of the damaged pieces are insured.
Calculate the probability that exactly two of the four damaged pieces are insured.
(A) 0.06
(B) 0.13
(C) 0.27
(D) 0.30
(E) 0.31
152. Automobile policies are separated into two groups: low-risk and high-risk. Actuary
Rahul examines low-risk policies, continuing until a policy with a claim is found and
then stopping. Actuary Toby follows the same procedure with high-risk policies. Each
low-risk policy has a 10% probability of having a claim. Each high-risk policy has a
20% probability of having a claim. The claim statuses of polices are mutually
independent.
Calculate the probability that Actuary Rahul examines fewer policies than Actuary Toby.
(A) 0.2857
(B) 0.3214
(C) 0.3333
(D) 0.3571
(E) 0.4000
Page 65 of 137
153. Let X represent the number of customers arriving during the morning hours and let Y
represent the number of customers arriving during the afternoon hours at a diner.
You are given:
i) X and Y are Poisson distributed.
ii) The first moment of X is less than the first moment of Y by 8.
iii) The second moment of X is 60% of the second moment of Y.
Calculate the variance of Y.
(A) 4
(B) 12
(C) 16
(D) 27
(E) 35
154. In a certain game of chance, a square board with area 1 is colored with sectors of either
red or blue. A player, who cannot see the board, must specify a point on the board by
giving an x-coordinate and a y-coordinate. The player wins the game if the specified
point is in a blue sector. The game can be arranged with any number of red sectors, and
the red sectors are designed so that
9
20
i
iR
 
  
 
, where iR is the area of the th
i red sector.
Calculate the minimum number of red sectors that makes the chance of a player winning
less than 20%.
(A) 3
(B) 4
(C) 5
(D) 6
(E) 7
Page 66 of 137
155. Automobile claim amounts are modeled by a uniform distribution on the interval [0,
10,000]. Actuary A reports X, the claim amount divided by 1000. Actuary B reports Y,
which is X rounded to the nearest integer from 0 to 10.
Calculate the absolute value of the difference between the 4th
moment of X and the 4th
moment of Y.
(A) 0
(B) 33
(C) 296
(D) 303
(E) 533
156. The probability of x losses occurring in year 1 is
1
(0.5) for 0,1,2, .x
x

The probability of y losses in year 2 given x losses in year 1 is given by the table:
Number of
losses in
Number of losses in year 2 (y)
given x losses in year 1
year 1 (x) 0 1 2 3 4+
0 0.60 0.25 0.05 0.05 0.05
1 0.45 0.30 0.10 0.10 0.05
2 0.25 0.30 0.20 0.20 0.05
3 0.15 0.20 0.20 0.30 0.15
4+ 0.05 0.15 0.25 0.35 0.20
Calculate the probability of exactly 2 losses in 2 years.
(A) 0.025
(B) 0.031
(C) 0.075
(D) 0.100
(E) 0.131
Page 67 of 137
157. Let X be a continuous random variable with density function
1
, 1
( )
0, otherwise
p
p
x
f x x


 

Calculate the value of p such that E(X) = 2.
(A) 1
(B) 2.5
(C) 3
(D) 5
(E) There is no such p.
158. The figure below shows the cumulative distribution function of a random variable, X.
Calculate E(X).
(A) 0.00
(B) 0.50
(C) 1.00
(D) 1.25
(E) 2.50
Page 68 of 137
159. Two fair dice are rolled. Let X be the absolute value of the difference between the two
numbers on the dice.
Calculate the probability that X < 3.
(A) 2/9
(B) 1/3
(C) 4/9
(D) 5/9
(E) 2/3
160. An actuary analyzes a company’s annual personal auto claims, M, and annual commercial
auto claims, N. The analysis reveals that Var(M) = 1600, Var(N) = 900, and the
correlation between M and N is 0.64.
Calculate Var(M + N).
(A) 768
(B) 2500
(C) 3268
(D) 4036
(E) 4420
161. An auto insurance policy has a deductible of 1 and a maximum claim payment of 5. Auto
loss amounts follow an exponential distribution with mean 2.
Calculate the expected claim payment made for an auto loss.
(A) 2 12
0.5 0.5e e 

(B) 32
1
72 

 ee
(C) 32
1
22 

 ee
(D) 2
1
2

e
(E) 32
1
23 

 ee
Page 69 of 137
162. The joint probability density function of X and Y is given by
, 0 2,0 2
( , ) 8
0, otherwise.
x y
x y
f x y

   
 

Calculate the variance of (X + Y)/2.
(A) 10/72
(B) 11/72
(C) 12/72
(D) 20/72
(E) 22/72
163. A student takes a multiple-choice test with 40 questions. The probability that the student
answers a given question correctly is 0.5, independent of all other questions. The
probability that the student answers more than N questions correctly is greater than 0.10.
The probability that the student answers more than N + 1 questions correctly is less than
0.10.
Calculate N using a normal approximation with the continuity correction.
(A) 23
(B) 25
(C) 32
(D) 33
(E) 35
164. In each of the months June, July, and August, the number of accidents occurring in that
month is modeled by a Poisson random variable with mean 1. In each of the other 9
months of the year, the number of accidents occurring is modeled by a Poisson random
variable with mean 0.5. Assume that these 12 random variables are mutually
independent.
Calculate the probability that exactly two accidents occur in July through November.
(A) 0.084
(B) 0.185
(C) 0.251
(D) 0.257
(E) 0.271
Page 70 of 137
165. Two claimants place calls simultaneously to an insurer’s claims call center. The times X
and Y, in minutes, that elapse before the respective claimants get to speak with call center
representatives are independently and identically distributed. The moment generating
function of each random variable is
2
1 2
( ) , .
1 1.5 3
M t t
t
 
  
 
Calculate the standard deviation of X + Y.
(A) 2.1
(B) 3.0
(C) 4.5
(D) 6.7
(E) 9.0
166. An airport purchases an insurance policy to offset costs associated with excessive
amounts of snowfall. For every full ten inches of snow in excess of 40 inches during the
winter season, the insurer pays the airport 300 up to a policy maximum of 700.
The following table shows the probability function for the random variable X of annual
(winter season) snowfall, in inches, at the airport.
Inches [0,20) [20,30) [30,40) [40,50) [50,60) [60,70) [70,80) [80,90) [90,inf)
Probability 0.06 0.18 0.26 0.22 0.14 0.06 0.04 0.04 0.00
Calculate the standard deviation of the amount paid under the policy.
(A) 134
(B) 235
(C) 271
(D) 313
(E) 352
Page 71 of 137
167. Damages to a car in a crash are modeled by a random variable with density function
2
( 60 800), 0 20
( )
0, otherwise
c x x x
f x
    
 

where c is a constant.
A particular car is insured with a deductible of 2. This car was involved in a crash with
resulting damages in excess of the deductible.
Calculate the probability that the damages exceeded 10.
(A) 0.12
(B) 0.16
(C) 0.20
(D) 0.26
(E) 0.78
168. Two fair dice, one red and one blue, are rolled.
Let A be the event that the number rolled on the red die is odd.
Let B be the event that the number rolled on the blue die is odd.
Let C be the event that the sum of the numbers rolled on the two dice is odd.
Determine which of the following is true.
(A) A, B, and C are not mutually independent, but each pair is independent.
(B) A, B, and C are mutually independent.
(C) Exactly one pair of the three events is independent.
(D) Exactly two of the three pairs are independent.
(E) No pair of the three events is independent.
Page 72 of 137
169. An urn contains four fair dice. Two have faces numbered 1, 2, 3, 4, 5, and 6; one has
faces numbered 2, 2, 4, 4, 6, and 6; and one has all six faces numbered 6. One of the dice
is randomly selected from the urn and rolled. The same die is rolled a second time.
Calculate the probability that a 6 is rolled both times.
(A) 0.174
(B) 0.250
(C) 0.292
(D) 0.380
(E) 0.417
170. An insurance agent meets twelve potential customers independently, each of whom is
equally likely to purchase an insurance product. Six are interested only in auto insurance,
four are interested only in homeowners insurance, and two are interested only in life
insurance.
The agent makes six sales.
Calculate the probability that two are for auto insurance, two are for homeowners
insurance, and two are for life insurance.
(A) 0.001
(B) 0.024
(C) 0.069
(D) 0.097
(E) 0.500
171. The return on two investments, X and Y, follows the joint probability density function
1/ 2, 0 1
( , )
0, otherwise.
x y
f x y
   
 

Calculate Var (X).
(A) 1/6
(B) 1/3
(C) 1/2
(D) 2/3
(E) 5/6
Page 73 of 137
172. A policyholder has probability 0.7 of having no claims, 0.2 of having exactly one claim,
and 0.1 of having exactly two claims. Claim amounts are uniformly distributed on the
interval [0, 60] and are independent. The insurer covers 100% of each claim.
Calculate the probability that the total benefit paid to the policyholder is 48 or less.
(A) 0.320
(B) 0.400
(C) 0.800
(D) 0.892
(E) 0.924
173. In a given region, the number of tornadoes in a one-week period is modeled by a Poisson
distribution with mean 2. The numbers of tornadoes in different weeks are mutually
independent.
Calculate the probability that fewer than four tornadoes occur in a three-week period.
(A) 0.13
(B) 0.15
(C) 0.29
(D) 0.43
(E) 0.86
174. An electronic system contains three cooling components that operate independently. The
probability of each component’s failure is 0.05. The system will overheat if and only if at
least two components fail.
Calculate the probability that the system will overheat.
(A) 0.007
(B) 0.045
(C) 0.098
(D) 0.135
(E) 0.143
Page 74 of 137
175. An insurance company’s annual profit is normally distributed with mean 100 and
variance 400.
Let Z be normally distributed with mean 0 and variance 1 and let F be the cumulative
distribution function of Z.
Determine the probability that the company’s profit in a year is at most 60, given that the
profit in the year is positive.
(A) 1 – F(2)
(B) F(2)/F(5)
(C) [1 – F(2)]/F(5)
(D) [F(0.25) – F(0.1)]/F(0.25)
(E) [F(5) – F(2)]/F(5)
176. In a group of health insurance policyholders, 20% have high blood pressure and 30%
have high cholesterol. Of the policyholders with high blood pressure, 25% have high
cholesterol.
A policyholder is randomly selected from the group.
Calculate the probability that a policyholder has high blood pressure, given that the
policyholder has high cholesterol.
(A) 1/6
(B) 1/5
(C) 1/4
(D) 2/3
(E) 5/6
Page 75 of 137
177. In a group of 25 factory workers, 20 are low-risk and five are high-risk.
Two of the 25 factory workers are randomly selected without replacement.
Calculate the probability that exactly one of the two selected factory workers is low-risk.
(A) 0.160
(B) 0.167
(C) 0.320
(D) 0.333
(E) 0.633
178. The proportion X of yearly dental claims that exceed 200 is a random variable with
probability density function
3 2
60 (1 ) , 0 1
( )
0, otherwise.
x x x
f x
   
 

Calculate Var[X/(1 – X)]
(A) 149/900
(B) 10/7
(C) 6
(D) 8
(E) 10
179. This year, a medical insurance policyholder has probability 0.70 of having no emergency
room visits, 0.85 of having no hospital stays, and 0.61 of having neither emergency room
visits nor hospital stays
Calculate the probability that the policyholder has at least one emergency room visit and
at least one hospital stay this year.
(A) 0.045
(B) 0.060
(C) 0.390
(D) 0.667
(E) 0.840
Page 76 of 137
180. An insurer offers a travelers insurance policy. Losses under the policy are uniformly
distributed on the interval [0, 5].
The insurer reimburses a policyholder for a loss up to a maximum of 4.
Determine the cumulative distribution function, F, of the benefit that the insurer pays a
policyholder who experiences exactly one loss under the policy.
(A)
0, 0
( ) 0.20 , 0 4
1 4
x
F x x x
x


  
 
(B)
0, 0
( ) 0.20 , 0 5
1 5
x
F x x x
x


  
 
(C)
0, 0
( ) 0.25 , 0 4
1 4
x
F x x x
x


  
 
(D)
0, 0
( ) 0.25 , 0 5
1 5
x
F x x x
x


  
 
(E)
0, 1
( ) 0.25 , 1 5
1 5
x
F x x x
x


  
 
181. A company issues auto insurance policies. There are 900 insured individuals. Fifty-four
percent of them are male. If a female is randomly selected from the 900, the probability
she is over 25 years old is 0.43. There are 395 total insured individuals over 25 years old.
A person under 25 years old is randomly selected.
Calculate the probability that the person selected is male.
(A) 0.47
(B) 0.53
(C) 0.54
(D) 0.55
(E) 0.56
Page 77 of 137
182. An insurance company insures red and green cars. An actuary compiles the following
data:
Color of car Red Green
Number insured 300 700
Probability an accident occurs 0.10 0.05
Probability that the claim exceeds
the deductible, given an accident
occurs from this group
0.90 0.80
The actuary randomly picks a claim from all claims that exceed the deductible.
Calculate the probability that the claim is on a red car.
(A) 0.300
(B) 0.462
(C) 0.491
(D) 0.667
(E) 0.692
183. X is a random variable with probability density function
2
4
, 0
( )
2 , 0
x
x
e x
f x
e x

 
 

Let 2
T X .
Determine the probability density function for T for positive values of t.
(A)
2 4
( )
2
t t
e e
f t
t t
 
 
(B)
2
( )
2
t
e
f t
t


(C)
2 4
( ) 2t t
f t e e 
 
(D)
2 2
2 4
( ) 2 4t t
f t te te 
 
(E)
2
2
( ) 2 t
f t te

Page 78 of 137
184. George and Paul play a betting game. Each chooses an integer from 1 to 20 (inclusive) at
random. If the two numbers differ by more than 3, George wins the bet. Otherwise, Paul
wins the bet.
Calculate the probability that Paul wins the bet.
(A) 0.27
(B) 0.32
(C) 0.40
(D) 0.48
(E) 0.66
185. A student takes an examination consisting of 20 true-false questions. The student knows
the answer to N of the questions, which are answered correctly, and guesses the answers
to the rest. The conditional probability that the student knows the answer to a question,
given that the student answered it correctly, is 0.824..
Calculate N.
(A) 8
(B) 10
(C) 14
(D) 16
(E) 18
186. The minimum force required to break a particular type of cable is normally distributed
with mean 12,432 and standard deviation 25. A random sample of 400 cables of this type
is selected.
Calculate the probability that at least 349 of the selected cables will not break under a
force of 12,400.
(A) 0.62
(B) 0.67
(C) 0.92
(D) 0.97
(E) 1.00
Page 79 of 137
187. The number of policies that an agent sells has a Poisson distribution with modes at 2 and
3.
K is the smallest number such that the probability of selling more than K policies is less
than 25%.
Calculate K.
(A) 1
(B) 2
(C) 3
(D) 4
(E) 5
188. Two fair dice are tossed. One die is red and one die is green.
Calculate the probability that the sum of the numbers on the two dice is an odd number
given that the number that shows on the red die is larger than the number that shows on
the green die.
(A) 1/4
(B) 5/12
(C) 3/7
(D) 1/2
(E) 3/5
189. In 1982 Abby’s mother scored at the 93rd percentile in the math SAT exam. In 1982 the
mean score was 503 and the variance of the scores was 9604.
In 2008 Abby took the math SAT and got the same numerical score as her mother had
received 26 years before. In 2008 the mean score was 521 and the variance of the scores
was 10,201.
Math SAT scores are normally distributed and stated in multiples of ten.
Calculate the percentile for Abby’s score.
(A) 89th
(B) 90th
(C) 91st
(D) 92nd
(E) 93rd
Page 80 of 137
190. A certain brand of refrigerator has a useful life that is normally distributed with mean 10
years and standard deviation 3 years. The useful lives of these refrigerators are
independent.
Calculate the probability that the total useful life of two randomly selected refrigerators
will exceed 1.9 times the useful life of a third randomly selected refrigerator.
(A) 0.407
(B) 0.444
(C) 0.556
(D) 0.593
(E) 0.604
191. A fire in an apartment building results in a loss, X, to the owner and a loss, Y, to the
tenants. The variables X and Y have a bivariate normal distribution with E(X) = 40,
Var(X) = 76, E(Y) = 30, Var(Y) = 32, and Var(X | Y = 28.5) = 57.
Calculate Var(Y | X = 25).
(A) 13
(B) 24
(C) 32
(D) 50
(E) 57
192. Losses covered by a flood insurance policy are uniformly distributed on the interval [0,
2]. The insurer pays the amount of the loss in excess of a deductible d.
The probability that the insurer pays at least 1.20 on a random loss is 0.30.
Calculate the probability that the insurer pays at least 1.44 on a random loss.
(A) 0.06
(B) 0.16
(C) 0.18
(D) 0.20
(E) 0.28
Page 81 of 137
193. The lifespan, in years, of a certain computer is exponentially distributed. The probability
that its lifespan exceeds four years is 0.30.
Let f(x) represent the density function of the computer’s lifespan, in years, for x > 0.
Determine which of the following is an expression for f(x).
(A)
/4
1 (0.3) x

(B) /4
1 (0.7)x

(C) /4
1 (0.3)x

(D) /4ln0.7
(0.7)
4
x

(E) /4ln0.3
(0.3)
4
x

194. The lifetime of a light bulb has density function, f, where f(x) is proportional to
2
3
, 0 5
1
x
x
x
 

, and 0, otherwise.
Calculate the mode of this distribution.
(A) 0.00
(B) 0.79
(C) 1.26
(D) 4.42
(E) 5.00
195. An insurer’s medical reimbursements have density function f, where f(x) is proportional
to
2
xxe , for 0 < x < 1, and 0, otherwise.
Calculate the mode of the medical reimbursements.
(A) 0.00
(B) 0.50
(C) 0.71
(D) 0.84
(E) 1.00
Page 82 of 137
196. A company has five employees on its health insurance plan. Each year, each employee
independently has an 80% probability of no hospital admissions. If an employee requires
one or more hospital admissions, the number of admissions is modeled by a geometric
distribution with a mean of 1.50. The numbers of hospital admissions of different
employees are mutually independent.
Each hospital admission costs 20,000.
Calculate the probability that the company’s total hospital costs in a year are less than
50,000.
(A) 0.41
(B) 0.46
(C) 0.58
(D) 0.69
(E) 0.78
197. On any given day, a certain machine has either no malfunctions or exactly one
malfunction. The probability of malfunction on any given day is 0.40. Machine
malfunctions on different days are mutually independent.
Calculate the probability that the machine has its third malfunction on the fifth day, given
that the machine has not had three malfunctions in the first three days.
(A) 0.064
(B) 0.138
(C) 0.148
(D 0.230
(E) 0.246
Page 83 of 137
198. In a certain group of cancer patients, each patient's cancer is classified in exactly one of
the following five stages: stage 0, stage 1, stage 2, stage 3, or stage 4.
i) 75% of the patients in the group have stage 2 or lower.
ii) 80% of the patients in the group have stage 1 or higher.
iii) 80% of the patients in the group have stage 0, 1, 3, or 4.
One patient from the group is randomly selected.
Calculate the probability that the selected patient's cancer is stage 1.
(A) 0.20
(B) 0.25
(C) 0.35
(D) 0.48
(E) 0.65
199. A car is new at the beginning of a calendar year. The time, in years, before the car
experiences its first failure is exponentially distributed with mean 2.
Calculate the probability that the car experiences its first failure in the last quarter of
some calendar year.
(A) 0.081
(B) 0.088
(C) 0.102
(D) 0.205
(E) 0.250
200. In a shipment of 20 packages, 7 packages are damaged. The packages are randomly
inspected, one at a time, without replacement, until the fourth damaged package is
discovered.
Calculate the probability that exactly 12 packages are inspected.
(A) 0.079
(B) 0.119
(C) 0.237
(D) 0.243
(E) 0.358
Page 84 of 137
201. A theme park conducts a study of families that visit the park during a year. The fraction
of such families of size m is 8
28
m , m = 1, 2, 3, 4, 5, 6, and 7.
For a family of size m that visits the park, the number of members of the family that ride
the roller coaster follows a discrete uniform distribution on the set {1, … , m}.
Calculate the probability that a family visiting the park has exactly six members, given
that exactly five members of the family ride the roller coaster.
(A) 0.17
(B) 0.21
(C) 0.24
(D) 0.28
(E) 0.31
202. The following information is given about a group of high-risk borrowers.
i) Of all these borrowers, 30% defaulted on at least one student loan.
ii) Of the borrowers who defaulted on at least one car loan, 40% defaulted on at least
one student loan.
iii) Of the borrowers who did not default on any student loans, 28% defaulted on at
least one car loan.
A statistician randomly selects a borrower from this group and observes that the selected
borrower defaulted on at least one student loan.
Calculate the probability that the selected borrower defaulted on at least one car loan.
(A) 0.33
(B) 0.40
(C) 0.44
(D) 0.65
(E) 0.72
Page 85 of 137
203. A machine has two components and fails when both components fail. The number of
years from now until the first component fails, X, and the number of years from now until
the machine fails, Y, are random variables with joint density function
 
( )/61
, 0
, 18
0, otherwise.
x y
e x y
f x y
 
 
 

Calculate Var(Y | X = 2).
(A) 6
(B) 8
(C) 36
(D) 45
(E) 64
204. The elapsed time, T, between the occurrence and the reporting of an accident has
probability density function
2
8
, 0 6
( ) 72
0, otherwise.
t t
t
f t
 
 
 


Given that T = t, the elapsed time between the reporting of the accident and payment by
the insurer is uniformly distributed on [2 + t, 10].
Calculate the probability that the elapsed time between the occurrence of the accident and
payment by the insurer is less than 4.
(A) 0.005
(B) 0.023
(C) 0.033
(D) 0.035
(E) 0.133
Page 86 of 137
205. The time until failure, T, of a product is modeled by a uniform distribution on [0, 10]. An
extended warranty pays a benefit of 100 if failure occurs between time t = 1.5 and t = 8.
The present value, W, of this benefit is
0.04
0, for 0 1.5
100 , for 1.5 8
0, for 8 10.
T
T
W e T
T

 

  
  
Calculate P(W < 79).
(A) 0.21
(B) 0.41
(C) 0.44
(D) 0.56
(E) 0.59
206. An insurance company issues policies covering damage to automobiles. The amount of
damage is modeled by a uniform distribution on [0, b].
The policy payout is subject to a deductible of b/10.
A policyholder experiences automobile damage.
Calculate the ratio of the standard deviation of the policy payout to the standard deviation
of the amount of the damage.
(A) 0.8100
(B) 0.9000
(C) 0.9477
(D) 0.9487
(E) 0.9735
Page 87 of 137
207 A policyholder purchases automobile insurance for two years. Define the following
events:
F = the policyholder has exactly one accident in year one.
G = the policyholder has one or more accidents in year two.
Define the following events:
i) The policyholder has exactly one accident in year one and has more than one
accident in year two.
ii) The policyholder has at least two accidents during the two-year period.
iii) The policyholder has exactly one accident in year one and has at least one
accident in year two.
iv) The policyholder has exactly one accident in year one and has a total of two or
more accidents in the two-year period.
v) The policyholder has exactly one accident in year one and has more accidents in
year two than in year one.
Determine the number of events from the above list of five that are the same as F G .
(A) None
(B) Exactly one
(C) Exactly two
(D) Exactly three
(E) All
208. An insurance company categorizes its policyholders into three mutually exclusive groups:
high-risk, medium-risk, and low-risk. An internal study of the company showed that
45% of the policyholders are low-risk and 35% are medium-risk. The probability of
death over the next year, given that a policyholder is high-risk is two times the
probability of death of a medium-risk policyholder. The probability of death over the
next year, given that a policyholder is medium-risk is three times the probability of death
of a low-risk policyholder. The probability of death of a randomly selected policyholder,
over the next year, is 0.009.
Calculate the probability of death of a policyholder over the next year, given that the
policyholder is high-risk.
(A) 0.0025
(B) 0.0200
(C) 0.1215
(D) 0.2000
(E) 0.3750
Page 88 of 137
209. A policy covers a gas furnace for one year. During that year, only one of three problems
can occur:
i) The igniter switch may need to be replaced at a cost of 60. There is a 0.10
probability of this.
ii) The pilot light may need to be replaced at a cost of 200. There is a 0.05
probability of this.
iii) The furnace may need to be replaced at a cost of 3000. There is a 0.01 probability
of this.
Calculate the deductible that would produce an expected claim payment of 30.
(A) 100
(B) At least 100 but less than 150
(C) At least 150 but less than 200
(D) At least 200 but less than 250
(E) At least 250
210. On a block of ten houses, k are not insured. A tornado randomly damages three houses
on the block.
The probability that none of the damaged houses are insured is 1/120.
Calculate the probability that at most one of the damaged houses is insured.
(A) 1/5
(B) 7/40
(C) 11/60
(D) 49/60
(E) 119/120
211. In a casino game, a gambler selects four different numbers from the first twelve positive
integers. The casino then randomly draws nine numbers without replacement from the
first twelve positive integers. The gambler wins the jackpot if the casino draws all four
of the gambler’s selected numbers.
Calculate the probability that the gambler wins the jackpot.
(A) 0.002
(B) 0.255
(C) 0.296
(D) 0.573
(E) 0.625
Page 89 of 137
212. The number of days an employee is sick each month is modeled by a Poisson distribution
with mean 1. The numbers of sick days in different months are mutually independent.
Calculate the probability that an employee is sick more than two days in a three-month
period.
(A) 0.199
(B) 0.224
(C) 0.423
(D) 0.577
(E) 0.801
213. The number of traffic accidents per week at intersection Q has a Poisson distribution with
mean 3. The number of traffic accidents per week at intersection R has a Poisson
distribution with mean 1.5.
Let A be the probability that the number of accidents at intersection Q exceeds its mean.
Let B be the corresponding probability for intersection R.
Calculate B – A.
(A) 0.00
(B) 0.09
(C) 0.13
(D) 0.19
(E) 0.31
214. Losses due to accidents at an amusement park are exponentially distributed. An
insurance company offers the park owner two different policies, with different premiums,
to insure against losses due to accidents at the park.
Policy A has a deductible of 1.44. For a random loss, the probability is 0.640 that under
this policy, the insurer will pay some money to the park owner. Policy B has a deductible
of d. For a random loss, the probability is 0.512 that under this policy, the insurer will
pay some money to the park owner.
Calculate d.
(A) 0.960
(B) 1.152
(C) 1.728
(D) 1.800
(E) 2.160
Page 90 of 137
215. The distribution of the size of claims paid under an insurance policy has probability
density function
, 0 5
( )
0, otherwise,
a
cx x
f x
  
 

Where a >0 and c > 0.
For a randomly selected claim, the probability that the size of the claim is less than 3.75
is 0.4871.
Calculate the probability that the size of a randomly selected claim is greater than 4.
(A) 0.404
(B) 0.428
(C) 0.500
(D) 0.572
(E) 0.596
216. Company XYZ provides a warranty on a product that it produces. Each year, the number
of warranty claims follows a Poisson distribution with mean c. The probability that no
warranty claims are received in any given year is 0.60.
Company XYZ purchases an insurance policy that will reduce its overall warranty claim
payment costs. The insurance policy will pay nothing for the first warranty claim
received and 5000 for each claim thereafter until the end of the year.
Calculate the expected amount of annual insurance policy payments to Company XYZ.
(A) 554
(B) 872
(C) 1022
(D) 1354
(E) 1612
Page 91 of 137
217. For a certain health insurance policy, losses are uniformly distributed on the interval
[0, b]. The policy has a deductible of 180 and the expected value of the unreimbursed
portion of a loss is 144.
Calculate b.
(A) 236
(B) 288
(C) 388
(D) 450
(E) 468
218. The working lifetime, in years, of a particular model of bread maker is normally
distributed with mean 10 and variance 4.
Calculate the 12th percentile of the working lifetime, in years.
(A) 5.30
(B) 7.65
(C) 8.41
(D) 12.35
(E) 14.70
219. The profits of life insurance companies A and B are normally distributed with the same
mean. The variance of company B's profit is 2.25 times the variance of company A's
profit. The 14th percentile of company A’s profit is the same as the pth percentile of
company B’s profit.
Calculate p.
(A) 5.3
(B) 9.3
(C) 21.0
(D) 23.6
(E) 31.6
Page 92 of 137
220. The distribution of values of the retirement package offered by a company to new
employees is modeled by the probability density function
1
( 5)
51
, 5
( ) 5
0, otherwise.
x
e x
f x
 

 


Calculate the variance of the retirement package value for a new employee, given that the
value is at least 10.
(A) 15
(B) 20
(C) 25
(D) 30
(E) 35
221. Insurance companies A and B each earn an annual profit that is normally distributed with
the same positive mean. The standard deviation of company A’s annual profit is one half
of its mean.
In a given year, the probability that company B has a loss (negative profit) is 0.9 times
the probability that company A has a loss.
Calculate the ratio of the standard deviation of company B’s annual profit to the standard
deviation of company A’s annual profit.
(A) 0.49
(B) 0.90
(C) 0.98
(D) 1.11
(E) 1.71
Page 93 of 137
222. Let X represent the number of policies sold by an agent in a day. The moment generating
function of X is
2 3 4
( ) 0.45 0.35 0.15 0.05 , fort t t t
M t e e e e t       .
Calculate the standard deviation of X.
(A) 0.76
(B) 0.87
(C) 1.48
(D) 1.80
(E) 4.00
223. Claim amounts at an insurance company are independent of one another. In year one,
claim amounts are modeled by a normal random variable X with mean 100 and standard
deviation 25. In year two, claim amounts are modeled by the random variable
Y = 1.04X + 5.
Calculate the probability that a random sample of 25 claim amounts in year two average
between 100 and 110.
(A) 0.48
(B) 0.53
(C) 0.54
(D) 0.67
(E) 0.68
224. Random variables X and Y are uniformly distributed on the region bounded by the x and y
axes, and the curve 2
1 xy  .
Calculate E(XY).
(A) 0.083
(B) 0.125
(C) 0.150
(D) 0.267
(E) 0.400
Page 94 of 137
225. An insurance company will cover losses incurred from tornadoes in a single calendar
year. However, the insurer will only cover losses for a maximum of three separate
tornadoes during this timeframe. Let X be the number of tornadoes that result in at least
50 million in losses, and let Y be the total number of tornadoes. The joint probability
function for X and Y is
( 2 ), for 0,1,2,3, 0,1,2,3,
( , )
0, otherwise,
c x y x y x y
p x y
   
 

where c is a constant.
Calculate the expected number of tornadoes that result in fewer than 50 million in losses.
(A) 0.19
(B) 0.28
(C) 0.76
(D) 1.00
(E) 1.10
226. At a polling booth, ballots are cast by ten voters, of whom three are Republicans, two are
Democrats, and five are Independents. A local journalist interviews two of these voters,
chosen randomly.
Calculate the expectation of the absolute value of the difference between the number of
Republicans interviewed and the number of Democrats interviewed.
(A) 1/5
(B) 7/15
(C) 3/5
(D) 11/15
(E) 1
Page 95 of 137
227. The random variables X and Y have joint probability function p(x,y) for x = 0,1 and
y = 0,1,2.
Suppose 3p(1,1) = p(1,2), and p(1,1) maximizes the variance of XY.
Calculate the probability that X or Y is 0.
(A) 11/25
(B) 23/50
(C) 23/49
(D) 26/49
(E) 14/25
228. As a block of concrete is put under increasing pressure, engineers measure the pressure X
at which the first fracture appears and the pressure Y at which the second fracture
appears. X and Y are measured in tons per square inch and have joint density function
24 (1 ), 0 1
( , )
0, otherwise.
x y x y
f x y
   
 

Calculate the average pressure (in tons per square inch) at which the second fracture
appears, given that the first fracture appears at 1/3 ton per square inch.
(A) 4/9
(B) 5/9
(C) 2/3
(D) 3/4
(E) 80/81
229. The number of severe storms that strike city J in a year follows a binomial distribution
with n = 5 and p = 0.6. Given that m severe storms strike city J in a year, the number
of severe storms that strike city K in the same year is m with probability 1/2, 1m
with probability 1/3, and 2m with probability 1/6.
Calculate the expected number of severe storms that strike city J in a year during which 5
severe storms strike city K.
(A) 3.5
(B) 3.7
(C) 3.9
(D) 4.0
(E) 5.7
Page 96 of 137
230. Let X denote the proportion of employees at a large firm who will choose to be covered
under the firm’s medical plan, and let Y denote the proportion who will choose to be
covered under both the firm’s medical and dental plans.
Suppose that for 0 1y x   , X and Y have the joint cumulative distribution function
2 2
( , ) ( )F x y y x xy y   .
Calculate the expected proportion of employees who will choose to be covered under
both plans.
(A) 0.06
(B) 0.33
(C) 0.42
(D) 0.50
(E) 0.75
231. Let N denote the number of accidents occurring during one month on the northbound side
of a highway and let S denote the number occurring on the southbound side.
Suppose that N and S are jointly distributed as indicated in the table.
N \ S 0 1 2 3 or more
0 0.04 0.06 0.10 0.04
1 0.10 0.18 0.08 0.03
2 0.12 0.06 0.05 0.02
3 or more 0.05 0.04 0.02 0.01
Calculate Var (N | N + S = 2).
(A) 0.48
(B) 0.55
(C) 0.67
(D) 0.91
(E) 1.25
Page 97 of 137
232. An insurance company sells automobile liability and collision insurance. Let X denote
the percentage of liability policies that will be renewed at the end of their terms and Y the
percentage of collision policies that will be renewed at the end of their terms. X and Y
have the joint cumulative distribution function
( )
( , ) , 0 100, 0 100.
2,000,000
xy x y
F x y x y

    
Calculate Var(X).
(A) 764
(B) 833
(C) 3402
(D) 4108
(E) 4167
233. A hurricane policy covers both water damage, X, and wind damage, Y, where X and Y
have joint density function
0.5 0.2 0.2 0.5 0.4 0.4
0.13 0.06 0.06 0.12 , 0, 0
( , )
0, otherwise.
x y x y x y x y
e e e e x y
f x y
       
     
 

Calculate the standard deviation of X.
(A) 1
(B) 2
(C) 3
(D) 4
(E) 5
Page 98 of 137
234. An insurance company has an equal number of claims in each of three territories. In each
territory, only three claim amounts are possible: 100, 500, and 1000. Based on the
company’s data, the probabilities of each claim amount are:
Claim Amount
100 500 1000
Territory 1 0.90 0.08 0.02
Territory 2 0.80 0.11 0.09
Territory 3 0.70 0.20 0.10
Calculate the standard deviation of a randomly selected claim amount.
(A) 254
(B) 291
(C) 332
(D) 368
(E) 396
235. At the start of a week, a coal mine has a high-capacity storage bin that is half full.
During the week, 20 loads of coal are added to the storage bin. Each load of coal has a
volume that is normally distributed with mean 1.50 cubic yards and standard deviation
0.25 cubic yards.
During the same week, coal is removed from the storage bin and loaded into 4 railroad
cars. The amount of coal loaded into each railroad car is normally distributed with mean
7.25 cubic yards and standard deviation 0.50 cubic yards.
The amounts added to the storage bin or removed from the storage bin are mutually
independent.
Calculate the probability that the storage bin contains more coal at the end of the week
than it had at the beginning of the week.
(A) 0.56
(B) 0.63
(C) 0.67
(D) 0.75
(E) 0.98
Page 99 of 137
236. An insurance company insures a good driver and a bad driver on the same policy. The
table below gives the probability of a given number of claims occurring for each of these
drivers in the next ten years.
Number
of claims
Probability for
the good driver
Probability for
the bad driver
0 0.5 0.2
1 0.3 0.3
2 0.2 0.4
3 0.0 0.1
The number of claims occurring for the two drivers are independent.
Calculate the mode of the distribution of the total number of claims occurring on this
policy in the next ten years.
(A) 0
(B) 1
(C) 2
(D) 3
(E) 4
237. In a group of 15 health insurance policyholders diagnosed with cancer, each policyholder
has probability 0.90 of receiving radiation and probability 0.40 of receiving
chemotherapy. Radiation and chemotherapy treatments are independent events for each
policyholder, and the treatments of different policyholders are mutually independent.
The policyholders in this group all have the same health insurance that pays 2 for
radiation treatment and 3 for chemotherapy treatment.
Calculate the variance of the total amount the insurance company pays for the radiation
and chemotherapy treatments for these 15 policyholders.
(A) 13.5
(B) 37.8
(C) 108.0
(D) 202.5
(E) 567.0
Page 100 of 137
238. In a large population of patients, 20% have early stage cancer, 10% have advanced stage
cancer, and the other 70% do not have cancer. Six patients from this population are
randomly selected.
Calculate the expected number of selected patients with advanced stage cancer, given that
at least one of the selected patients has early stage cancer.
(A) 0.403
(B) 0.500
(C) 0.547
(D) 0.600
(E) 0.625
239. Four distinct integers are chosen randomly and without replacement from the first twelve
positive integers. Let X be the random variable representing the second largest of the
four selected integers, and let p be the probability function for X.
Determine p(x), for integer values of x, where p(x) > 0.
(A)
( 1)( 2)(12 )
990
x x x  
(B)
( 1)( 2)(12 )
495
x x x  
(C)
( 1)(12 )(11 )
495
x x x  
(D)
( 1)(12 )(11 )
990
x x x  
(E)
(10 )(12 )(11 )
990
x x x  
240. An insurance policy covers losses incurred by a policyholder, subject to a deductible of
10,000. Incurred losses follow a normal distribution with mean 12,000 and standard
deviation c. The probability that a loss is less than k is 0.9582, where k is a constant.
Given that the loss exceeds the deductible, there is a probability of 0.9500 that it is less
than k.
Calculate c.
(A) 2045
(B) 2267
(C) 2393
(D) 2505
(E) 2840
Page 101 of 137
241. Losses covered by an insurance policy are modeled by a uniform distribution on the
interval [0, 1000]. An insurance company reimburses losses in excess of a deductible of
250.
Calculate the difference between the median and the 20th percentile of the insurance
company reimbursement, over all losses.
(A) 225
(B) 250
(C) 300
(D) 375
(E) 500
242. The annual profits that company A and company B earn follow a bivariate normal
distribution.
Company A's annual profit has mean 2000 and standard deviation 1000.
Company B's annual profit has mean 3000 and standard deviation 500.
The correlation coefficient between these annual profits is 0.80.
Calculate the probability that company B's annual profit is less than 3900, given that
company A's annual profit is 2300.
(A) 0.8531
(B) 0.9192
(C) 0.9641
(D) 0.9744
(E) 0.9953
Page 102 of 137
243. An insurance agent’s files reveal the following facts about his policyholders:
i) 243 own auto insurance.
ii) 207 own homeowner insurance.
iii) 55 own life insurance and homeowner insurance.
iv) 96 own auto insurance and homeowner insurance.
v) 32 own life insurance, auto insurance and homeowner insurance.
vi) 76 more clients own only auto insurance than only life insurance.
vii) 270 own only one of these three insurance products.
Calculate the total number of the agent’s policyholders who own at least one of these
three insurance products.
(A) 389
(B) 407
(C) 423
(D) 448
(E) 483
244. A profile of the investments owned by an agent’s clients follows:
i) 228 own annuities.
ii) 220 own mutual funds.
iii) 98 own life insurance and mutual funds.
iv) 93 own annuities and mutual funds.
v) 16 own annuities, mutual funds, and life insurance.
vi) 45 more clients own only life insurance than own only annuities.
vii) 290 own only one type of investment (i.e., annuity, mutual fund, or life
insurance).
Calculate the agent’s total number of clients.
(A) 455
(B) 495
(C) 496
(D) 500
(E) 516
Page 103 of 137
245. An insurance policy pays 100 per day for up to three days of hospitalization and 50 per
day for each day of hospitalization thereafter.
The number of days of hospitalization, X, is a discrete random variable with probability
function
 
6
, for 1,2,3,4,5
15
0, otherwise.
k
k
P X k


  

Calculate the expected payment for hospitalization under this policy.
(A) 123
(B) 210
(C) 220
(D) 270
(E) 367
246. An actuary compiles the following information from a portfolio of 1000 homeowners
insurance policies:
i) 130 policies insure three-bedroom homes.
ii) 280 policies insure one-story homes.
iii) 150 policies insure two-bath homes.
iv) 30 policies insure three-bedroom, two-bath homes.
v) 50 policies insure one-story, two-bath homes.
vi) 40 policies insure three-bedroom, one-story homes.
vii) 10 policies insure three-bedroom, one-story, two-bath homes.
Calculate the number of homeowners policies in the portfolio that insure neither onestory
nor two-bath nor three-bedroom homes.
(A) 310
(B) 450
(C) 530
(D) 550
(E) 570
Page 104 of 137
247. Each week, a subcommittee of four individuals is formed from among the members of a
committee comprising seven individuals. Two subcommittee members are then assigned
to lead the subcommittee, one as chair and the other as secretary.
Calculate the maximum number of consecutive weeks that can elapse without having the
subcommittee contain four individuals who have previously served together with the
same subcommittee chair.
(A) 70
(B) 140
(C) 210
(D) 420
(E) 840
248. Bowl I contains eight red balls and six blue balls. Bowl II is empty. Four balls are
selected at random, without replacement, and transferred from bowl I to bowl II. One
ball is then selected at random from bowl II.
Calculate the conditional probability that two red balls and two blue balls were
transferred from bowl I to bowl II, given that the ball selected from bowl II is blue.
(A) 0.21
(B) 0.24
(C) 0.43
(D) 0.49
(E) 0.57
Page 105 of 137
249. An actuary has done an analysis of all policies that cover two cars. 70% of the policies
are of type A for both cars, and 30% of the policies are of type B for both cars. The
number of claims on different cars across all policies are mutually independent. The
distributions of the number of claims on a car are given in the following table.
Number of
Claims
Type A Type B
0 40% 25%
1 30% 25%
2 20% 25%
3 10% 25%
Four policies are selected at random.
Calculate the probability that exactly one of the four policies has the same number of
claims on both covered cars.
(A) 0.104
(B) 0.250
(C) 0.285
(D) 0.417
(E) 0.739
250. A company sells two types of life insurance policies (P and Q) and one type of health
insurance policy. A survey of potential customers revealed the following:
i) No survey participant wanted to purchase both life policies.
ii) Twice as many survey participants wanted to purchase life policy P as life policy
Q.
iii) 45% of survey participants wanted to purchase the health policy.
iv) 18% of survey participants wanted to purchase only the health policy.
v) The event that a survey participant wanted to purchase the health policy was
independent of the event that a survey participant wanted to purchase a life policy.
Calculate the probability that a randomly selected survey participant wanted to purchase
exactly one policy.
(A) 0.51
(B) 0.60
(C) 0.69
(D) 0.73
(E) 0.78
Page 106 of 137
251. A state is starting a lottery game. To enter this lottery, a player uses a machine that
randomly selects six distinct numbers from among the first 30 positive integers. The
lottery randomly selects six distinct numbers from the same 30 positive integers. A
winning entry must match the same set of six numbers that the lottery selected.
The entry fee is 1, each winning entry receives a prize amount of 500,000, and all other
entries receive no prize.
Calculate the probability that the state will lose money, given that 800,000 entries are
purchased.
(A) 0.33
(B) 0.39
(C) 0.61
(D) 0.67
(E) 0.74
252. A life insurance company has found there is a 3% probability that a randomly selected
application contains an error. Assume applications are mutually independent in this
respect.
An auditor randomly selects 100 applications.
Calculate the probability that 95% or less of the selected applications are error-free.
(A) 0.08
(B) 0.10
(C) 0.13
(D) 0.15
(E) 0.18
Page 107 of 137
253. Let A, B, and C be events such that P[A] = 0.2, P[B] = 0.1, and P[C] = 0.3. The events
A and B are independent, the events B and C are independent, and the events A and C are
mutually exclusive.
Calculate [A B C]P   .
(A) 0.496
(B) 0.540
(C) 0.544
(D) 0.550
(E) 0.600
254. The annual numbers of thefts a homeowners insurance policyholder experiences are
analyzed over three years.
Define the following events:
i) A = the event that the policyholder experiences no thefts in the three years.
ii) B = the event that the policyholder experiences at least one theft in the second
year.
iii) C = the event that the policyholder experiences exactly one theft in the first year.
iv) D = the event that the policyholder experiences no thefts in the third year.
v) E = the event that the policyholder experiences no thefts in the second year, and at
least one theft in the third year.
Determine which three events satisfy the condition that the probability of their union
equals the sum of their probabilities.
(A) Events A, B, and E
(B) Events A, C, and E
(C) Events A, D, and E
(D) Events B, C, and D
(E) Events B, C, and E
Page 108 of 137
255. Four letters to different insureds are prepared along with accompanying envelopes. The
letters are put into the envelopes randomly.
Calculate the probability that at least one letter ends up in its accompanying envelope.
(A) 27/256
(B) 1/4
(C) 11/24
(D) 5/8
(E) 3/4
256. A health insurance policy covers visits to a doctor’s office. Each visit costs 100. The
annual deductible on the policy is 350. For a policy, the number of visits per year has the
following probability distribution:
Number of Visits 0 1 2 3 4 5 6
Probability 0.60 0.15 0.10 0.08 0.04 0.02 0.01
A policy is selected at random from those where costs exceed the deductible.
Calculate the probability that this policyholder had exactly five office visits.
(A) 0.050
(B) 0.133
(C) 0.286
(D) 0.333
(E) 0.429
257. A machine has two parts labelled A and B. The probability that part A works for one
year is 0.8 and the probability that part B works for one year is 0.6. The probability that
at least one part works for one year is 0.9.
Calculate the probability that part B works for one year, given that part A works for one
year.
(A) 1/2
(B) 3/5
(C) 5/8
(D) 3/4
(E) 5/6
Page 109 of 137
258. Six claims are to be randomly selected from a group of thirteen different claims, which
includes two workers compensation claims, four homeowners claims and seven auto
claims.
Calculate the probability that the six claims selected will include one workers
compensation claim, two homeowners claims and three auto claims.
(A) 0.025
(B) 0.107
(C) 0.153
(D) 0.245
(E) 0.643
259. A drawer contains four pairs of socks, with each pair a different color. One sock at a
time is randomly drawn from the drawer until a matching pair is obtained.
Calculate the probability that the maximum number of draws is required.
(A) 0.0006
(B) 0.0095
(C) 0.0417
(D) 0.1429
(E) 0.2286
260. At a mortgage company, 60% of calls are answered by an attendant. The remaining 40%
of callers leave their phone numbers. Of these 40%, 75% receive a return phone call the
same day. The remaining 25% receive a return call the next day.
Of those who initially spoke to an attendant, 80% will apply for a mortgage. Of those
who received a return call the same day, 60% will apply. Of those who received a return
call the next day, 40% will apply.
Calculate the probability that a person initially spoke to an attendant, given that he or she
applied for a mortgage.
(A) 0.06
(B) 0.26
(C) 0.48
(D) 0.60
(E) 0.69
Page 110 of 137
261. An insurance company studies back injury claims from a manufacturing company. The
insurance company finds that 40% of workers do no lifting on the job, 50% do moderate
lifting and 10% do heavy lifting.
During a given year, the probability of filing a claim is 0.05 for a worker who does no
lifting, 0.08 for a worker who does moderate lifting and 0.20 for a worker who does
heavy lifting.
A worker is chosen randomly from among those who have filed a back injury claim.
Calculate the probability that the worker’s job involves moderate or heavy lifting.
(A) 0.75
(B) 0.81
(C) 0.85
(D) 0.86
(E) 0.89
262. The number of traffic accidents occurring on any given day in Coralville is Poisson
distributed with mean 5. The probability that any such accident involves an uninsured
driver is 0.25, independent of all other such accidents.
Calculate the probability that on a given day in Coralville there are no traffic accidents
that involve an uninsured driver.
(A) 0.007
(B) 0.010
(C) 0.124
(D) 0.237
(E) 0.287
Page 111 of 137
263. A group of 100 patients is tested, one patient at a time, for three risk factors for a certain
disease until either all patients have been tested or a patient tests positive for more than
one of these three risk factors. For each risk factor, a patient tests positive with
probability p, where 0 < p < 1. The outcomes of the tests across all patients and all risk
factors are mutually independent.
Determine an expression for the probability that exactly n patients are tested, where n is a
positive integer less than 100.
(A)
12 2
1 3 (1 ) 3 (1 )
n
p p p p

        
(B)
12 3 2 3
1 3 (1 ) 3 (1 )
n
p p p p p p

          
(C)
12 3 2 3
1 3 (1 ) 3 (1 )
n
p p p p p p

          
(D)
12 3 2 3
1 3 (1 ) 3 (1 )
n
n p p p p p p

          
(E)
2 31 1 1
3 (1 ) 1 (1 ) (1 )n n n
p p p p p p  
              
264. A representative of a market research firm contacts consumers by phone in order to
conduct surveys. The specific consumer contacted by each phone call is randomly
determined. The probability that a phone call produces a completed survey is 0.25.
Calculate the probability that more than three phone calls are required to produce one
completed survey.
(A) 0.32
(B) 0.42
(C) 0.44
(D) 0.56
(E) 0.58
Page 112 of 137
265. Four distinct integers are chosen randomly and without replacement from the first twelve
positive integers. X is the random variable representing the second smallest of the four
selected integers, and p is the probability function of X.
Determine p(x) for x = 2,3,…,10.
(A)
( 1)(11 )(12 )
495
x x x  
(B)
( 1)(11 )(12 )
990
x x x  
(C)
( 1)( 2)(12 )
990
x x x  
(D)
( 1)( 2)(12 )
495
x x x  
(E)
(10 )(11 )(12 )
495
x x x  
266. Losses due to burglary are exponentially distributed with mean 100.
The probability that a loss is between 40 and 50 equals the probability that a loss is
between 60 and r, with r > 60.
Calculate r.
(A) 68.26
(B) 70.00
(C) 70.51
(D) 72.36
(E) 75.00
Page 113 of 137
267. The time until the next car accident for a particular driver is exponentially distributed
with a mean of 200 days.
Calculate the probability that the driver has no accidents in the next 365 days, but then
has at least one accident in the 365-day period that follows this initial 365-day period.
(A) 0.026
(B) 0.135
(C) 0.161
(D) 0.704
(E) 0.839
268. The annual profit of a life insurance company is normally distributed.
The probability that the annual profit does not exceed 2000 is 0.7642. The probability
that the annual profit does not exceed 3000 is 0.9066.
Calculate the probability that the annual profit does not exceed 1000.
(A) 0.1424
(B) 0.3022
(C) 0.5478
(D) 0.6218
(E) 0.7257
Page 114 of 137
269. Individuals purchase both collision and liability insurance on their automobiles. The
value of the insured’s automobile is V. Assume the loss L on an automobile claim is a
random variable with cumulative distribution function
 
3
3
, 0
4
( )
1
1 , .
10
l V
V
l
l V
V
F l
e V l
 
  
   
   

 
Calculate the probability that the loss on a randomly selected claim is greater than the
value of the automobile.
(A) 0.00
(B) 0.10
(C) 0.25
(D) 0.75
(E) 0.90
270. The lifetime of a machine part is exponentially distributed with a mean of five years.
Calculate the mean lifetime of the part, given that it survives less than ten years.
(A) 0.865
(B) 1.157
(C) 2.568
(D) 2.970
(E) 3.435
271. Let X be a random variable with density function
2
2 , 0
( )
0, otherwise.
x
e x
f x

 
 

Calculate [ 0.5 1.0]P X X  .
(A) 0.433
(B) 0.547
(C) 0.632
(D) 0.731
(E) 0.865
Page 115 of 137
272. Events E and F are independent. P[E] = 0.84 and P[F] = 0.65.
Calculate the probability that exactly one of the two events occurs.
(A) 0.056
(B) 0.398
(C) 0.546
(D) 0.650
(E) 0.944
273. A flood insurance company determines that N, the number of claims received in a month,
is a random variable with 1
2
[ ] , for 0,1,2, .
3n
P N n n
   The numbers of claims
received in different months are mutually independent.
Calculate the probability that more than three claims will be received during a
consecutive two-month period, given that fewer than two claims were received in the first
of the two months.
(A) 0.0062
(B) 0.0123
(C) 0.0139
(D) 0.0165
(E) 0.0185
274. Patients in a study are tested for sleep apnea, one at a time, until a patient is found to have
this disease. Each patient independently has the same probability of having sleep apnea.
Let r represent the probability that at least four patients are tested.
Determine the probability that at least twelve patients are tested given that at least four
patients are tested.
(A) 3
11
r
(B)
3
r
(C) 3
8
r
(D)
2
r
(E) 3
1
r
Page 116 of 137
275. A factory tests 100 light bulbs for defects. The probability that a bulb is defective is 0.02.
The occurrences of defects among the light bulbs are mutually independent events.
Calculate the probability that exactly two are defective given that the number of defective
bulbs is two or fewer.
(A) 0.133
(B) 0.271
(C) 0.273
(D) 0.404
(E) 0.677
276. A certain town experiences an average of 5 tornadoes in any four year period. The
number of years from now until the town experiences its next tornado as well as the
number of years between tornados have identical exponential distributions and all such
times are mutually independent
Calculate the median number of years from now until the town experiences its next
tornado.
(A) 0.55
(B) 0.73
(C) 0.80
(D) 0.87
(E) 1.25
277. Losses under an insurance policy are exponentially distributed with mean 4. The
deductible is 1 for each loss.
Calculate the median amount that the insurer pays a policyholder for a loss under the
policy.
(A) 1.77
(B) 2.08
(C) 2.12
(D) 2.77
(E) 3.12
Page 117 of 137
278. A company has purchased a policy that will compensate for the loss of revenue due to
severe weather events. The policy pays 1000 for each severe weather event in a year
after the first two such events in that year. The number of severe weather events per year
has a Poisson distribution with mean 1.
Calculate the expected amount paid to this company in one year.
(A) 80
(B) 104
(C) 368
(D) 512
(E) 632
279. A company provides each of its employees with a death benefit of 100. The company
purchases insurance that pays the cost of total death benefits in excess of 400 per year.
The number of employees who will die during the year is a Poisson random variable with
mean 2.
Calculate the expected annual cost to the company of providing the death benefits,
excluding the cost of the insurance.
(A) 171
(B) 189
(C) 192
(D) 200
(E) 208
280. The number of burglaries occurring on Burlington Street during a one-year period is
Poisson distributed with mean 1.
Calculate the expected number of burglaries on Burlington Street in a one-year period,
given that there are at least two burglaries.
(A) 0.63
(B) 2.39
(C) 2.54
(D) 3.00
(E) 3.78
Page 118 of 137
281. For a certain health insurance policy, losses are uniformly distributed on the interval
[0, 450]. The policy has a deductible of d and the expected value of the unreimbursed
portion of a loss is 56.
Calculate d.
(A) 60
(B) 87
(C) 112
(D) 169
(E) 224
282. A motorist just had an accident. The accident is minor with probability 0.75 and is
otherwise major.
Let b be a positive constant. If the accident is minor, then the loss amount follows a
uniform distribution on the interval [0, b]. If the accident is major, then the loss amount
follows a uniform distribution on the interval [b, 3b].
The median loss amount due to this accident is 672.
Calculate the mean loss amount due to this accident.
(A) 392
(B) 512
(C) 672
(D) 882
(E) 1008
Page 119 of 137
283. An insurance policy will reimburse only one claim per year.
For a random policyholder, there is a 20% probability of no loss in the next year, in
which case the claim amount is 0. If a loss occurs in the next year, the claim amount is
normally distributed with mean 1000 and standard deviation 400.
Calculate the median claim amount in the next year for a random policyholder.
(A) 663
(B) 790
(C) 873
(D) 994
(E) 1000
284. Losses incurred by a policyholder follow a normal distribution with mean 20,000 and
standard deviation 4,500. The policy covers losses, subject to a deductible of 15,000.
Calculate the 95th
percentile of losses that exceed the deductible.
(A) 27,400
(B) 27,700
(C) 28,100
(D) 28,400
(E) 28,800
285. A gun shop sells gunpowder. Monthly demand for gunpowder is normally distributed,
averages 20 pounds, and has a standard deviation of 2 pounds. The shop manager wishes
to stock gunpowder inventory at the beginning of each month so that there is only a 2%
chance that the shop will run out of gunpowder (i.e., that demand will exceed inventory)
in any given month.
Calculate the amount of gunpowder to stock in inventory, in pounds.
(A) 16
(B) 23
(C) 24
(D) 32
(E) 43
Page 120 of 137
286. A large university will begin a 13-day period during which students may register for that
semester’s courses. Of those 13 days, the number of elapsed days before a randomly
selected student registers has a continuous distribution with density function f(t) that is
symmetric about t = 6.5 and proportional to 1/(t + 1) between days 0 and 6.5.
A student registers at the 60th percentile of this distribution.
Calculate the number of elapsed days in the registration period for this student.
(A) 4.01
(B) 7.80
(C) 8.99
(D) 10.22
(E) 10.51
287. The loss L due to a boat accident is exponentially distributed.
Boat insurance policy A covers up to 1 unit for each loss. Boat insurance policy B covers
up to 2 units for each loss.
The probability that a loss is fully covered under policy B is 1.9 times the probability that
it is fully covered under policy A.
Calculate the variance of L.
(A) 0.1
(B) 0.4
(C) 2.4
(D) 9.5
(E) 90.1
Page 121 of 137
288. Losses, X, under an insurance policy are exponentially distributed with mean 10. For
each loss, the claim payment Y is equal to the amount of the loss in excess of a deductible
d > 0.
Calculate Var(Y).
(A) 100 - d
(B) 2
(10 )d
(C) /10
100 d
e
(D)  /10 /5
100 2 d d
e e 

(E)  2 /10 /5
(10 ) 2 d d
d e e 
 
289. For a certain insurance company, 10% of its policies are Type A, 50% are Type B, and
40% are Type C.
The annual number of claims for an individual Type A, Type B, and Type C policy
follow Poisson distributions with respective means 1, 2, and 10.
Let X represent the annual number of claims of a randomly selected policy.
Calculate the variance of X.
(A) 5.10
(B) 16.09
(C) 21.19
(D) 42.10
(E) 47.20
290. The number of tornadoes in a given year follows a Poisson distribution with mean 3.
Calculate the variance of the number of tornadoes in a year given that at least one tornado
occurs.
(A) 1.63
(B) 1.73
(C) 2.66
(D) 3.00
(E) 3.16
Page 122 of 137
291. A government employee’s yearly dental expense follows a uniform distribution on the
interval from 200 to 1200. The government’s primary dental plan reimburses an
employee for up to 400 of dental expense incurred in a year, while a supplemental plan
pays up to 500 of any remaining dental expense.
Let Y represent the yearly benefit paid by the supplemental plan to a government
employee.
Calculate Var(Y).
(A) 20,833
(B) 26,042
(C) 41,042
(D) 53,333
(E) 83,333
292. Under a liability insurance policy, losses are uniformly distributed on [0, b], where b is a
positive constant. There is a deductible of b/2.
Calculate the ratio of the variance of the claim payment (greater than or equal to zero)
from a given loss to the variance of the loss.
(A) 1:8
(B) 3:16
(C) 1:4
(D) 5:16
(E) 1:2
293. A company’s annual profit, in billions, has a normal distribution with variance equal to
the cube of its mean. The probability of an annual loss is 5%.
Calculate the company’s expected annual profit.
(A) 370 million
(B) 520 million
(C) 780 million
(D) 950 million
(E) 1645 million
Page 123 of 137
294. The number of claims X on a health insurance policy is a random variable with
2
E[ ] 61X  and 2
[( 1) ] 47E X   .
Calculate the standard deviation of the number of claims.
(A) 2.18
(B) 2.40
(C) 7.31
(D) 7.50
(E) 7.81
295. Ten cards from a deck of playing cards are in a box: two diamonds, three spades, and five
hearts. Two cards are randomly selected without replacement.
Calculate the variance of the number of diamonds selected, given that no spade is
selected.
(A) 0.24
(B) 0.28
(C) 0.32
(D) 0.34
(E) 0.41
Page 124 of 137
296. A homeowners insurance policy covers losses due to theft, with a deductible of 3. Theft
losses are uniformly distributed on [0, 10].
Determine the moment generating function, M(t), for t ≠ 0, of the claim payment on a
theft.
(A)
7
3 1
10 10
t
e
t


(B)
10
1
3
10
t
e
t


(C)
7 3
10
t t
e e
t


(D)
10
1
min 0, 3
10
t
e
t
 
 
 
(E)
10
1
max 0, 3
10
t
e
t
 
 
 
297. The random variable X has moment generating function M(t).
Determine which of the following is the moment generating function of some random
variable.
i) ( ) (5 )M t M t
ii) 2 ( )M t
iii) ( )t
e M t
(A) at most one of i, ii, and iii
(B) i and ii only
(C) i and iii only
(D) ii and iii only
(E) i, ii, and iii
Page 125 of 137
298. The number of boating accidents a policyholder experiences this year is modeled by a
Poisson random variable with variance 0.10.
An insurer reimburses only the first accident.
Let Y be the number of unreimbursed accidents the policyholder experiences this year
and let p be the probability function of Y.
Determine p(y).
(A)
0.1
1
0.1
1.1 , 0
( ) (0.1)
, 1,2,3,
( 1)!
y
e y
p y
e y
y



 

 
 
(B)
0.1
0.1
1.1 , 0
( ) (0.1)
1, 1,2,3,
!
y
e y
p y
e y
y


 

 
 

(C)
0.1
1
0.1
1.1 , 0
( ) (0.1)
, 1,2,3,
( 1)!
y
e y
p y
e y
y



 

 
 
(D)
1
0.1(0.1)
( ) , 0,1,2,
( 1)!
y
p y e y
y


 

(E)
1
0.1(0.1)
( ) , 0,1,2,
( 1)!
y
p y e y
y


 

Page 126 of 137
299. Let X be a continuous random variable with probability density function
, 0
( )
0, otherwise,
x
e x
f x

 
 
 

where 0. 
Let Y be the smallest integer greater than or equal to X.
Determine the probability function of Y.
(A) 1 , 1,2,3,y
e y
 
(B) ( 1), 1,2,3,y
e e y 
 
(C) (1 ), 1,2,3,y
e e y  
 
(D) , 1,2,3,y
e y
 

(E) , 1,2,3,
!
y
e
y
y



300. Batteries A and B have lifetimes that are independent and exponentially distributed with
a common mean of m years. The probability that battery B outlasts battery A by more
than one year is 0.33.
Calculate m.
(A) 0.42
(B) 0.59
(C) 0.90
(D) 1.80
(E) 2.41
Page 127 of 137
301. A car and a bus arrive at a railroad crossing at times independently and uniformly
distributed between 7:15 and 7:30. A train arrives at the crossing at 7:20 and halts traffic
at the crossing for five minutes.
Calculate the probability that the waiting time of the car or the bus at the crossing
exceeds three minutes.
(A) 0.25
(B) 0.27
(C) 0.36
(D) 0.40
(E) 0.56
302. An insurance company sells automobile liability and collision insurance. Let X denote
the percentage of liability policies that will be renewed at the end of their terms and Y the
percentage of collision policies that will be renewed at the end of their terms. X and Y
have a joint cumulative distribution
( )
( , ) , 0 100,0 100.
2,000,000
xy x y
F x y x y

    
Calculate the probability that at least 80% of liability policies and 80% of collision
policies will be renewed at the end of their terms.
(A) 0.072
(B) 0.200
(C) 0.280
(D) 0.360
(E) 0.488
Page 128 of 137
303. Skateboarders A and B practice one difficult stunt until becoming injured while
attempting the stunt. On each attempt, the probability of becoming injured is p,
independent of the outcomes of all previous attempts.
Let F(x, y) represent the probability that skateboarders A and B make no more than x and
y attempts, respectively, where x and y are positive integers.
It is given that F(2, 2) = 0.0441.
Calculate F(1, 5).
(A) 0.0093
(B) 0.0216
(C) 0.0495
(D) 0.0551
(E) 0.1112
304. The number of minor surgeries, X, and the number of major surgeries, Y, for a
policyholder, this decade, has joint cumulative distribution function
1 1
( , ) 1 (0.5) 1 (0.2) ,x y
F x y  
        
for nonnegative integers x and y.
Calculate the probability that the policyholder experiences exactly three minor surgeries
and exactly three major surgeries this decade.
(A) 0.00004
(B) 0.00040
(C) 0.03244
(D) 0.06800
(E) 0.12440
Page 129 of 137
305. A company provides a death benefit of 50,000 for each of its 1000 employees. There is a
1.4% chance that any one employee will die next year, independent of all other
employees. The company establishes a fund such that the probability is at least 0.99 that
the fund will cover next year’s death benefits.
Calculate, using the Central Limit Theorem, the smallest amount of money, rounded to
the nearest 50 thousand, that the company must put into the fund.
(A) 750,000
(B) 850,000
(C) 1,050,000
(D) 1,150,000
(E) 1,400,000
306. An investor invests 100 dollars in a stock. Each month, the investment has probability
0.5 of increasing by 1.10 dollars and probability 0.5 of decreasing by 0.90 dollars. The
changes in price in different months are mutually independent.
Calculate the probability that the investment has a value greater than 91 dollars at the end
of month 100.
(A) 0.63
(B) 0.75
(C) 0.82
(D) 0.94
(E) 0.97
307. The number of workplace accidents occurring in a factory on any given day is Poisson
distributed with mean  . The parameter  is a random variable that is determined by
the level of activity in the factory and is uniformly distributed on the interval [0,3].
Calculate the probability of one accident on a given day.
(A) 0.179
(B) 0.267
(C) 0.317
(D) 0.335
(E) 0.368
Page 130 of 137
308. Let X denote the loss amount sustained by an insurance company’s policyholder in an
auto collision. Let Z denote the portion of X that the insurance company will have to pay.
An actuary determines that X and Z are independent with respective density and
probability functions
/8
(1/ 8) , 0
( )
0, otherwise
x
e x
f x

 
 

and
0.45, 1
[ ]
0.55, 0.
z
P Z z
z

  

Calculate the variance of the insurance company’s claim payment ZX.
(A) 13.0
(B) 15.8
(C) 28.8
(D) 35.2
(E) 44.6
309. A city with borders forming a square with sides of length 1 has its city hall located at the
origin when a rectangular coordinate system is imposed on the city so that two sides of
the square are on the positive axes. The density function of the population is
2 2
1.5( ), 0 , 1
( , )
0, otherwise.
x y x y
f x y
   
 

A resident of the city can travel to the city hall only along a route whose segments are
parallel to the city borders.
Calculate the expected value of the travel distance to the city hall of a randomly chosen
resident of the city.
(A) 1/2
(B) 3/4
(C) 1
(D) 5/4
(E) 3/2
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310. A couple takes out a medical insurance policy that reimburses them for days of work
missed due to illness. Let X and Y denote the number of days missed during a given
month by the wife and husband, respectively. The policy pays a monthly benefit of 50
times the maximum of X and Y, subject to a benefit limit of 100. X and Y are
independent, each with a discrete uniform distribution on the set {0,1,2,3,4}.
Calculate the expected monthly benefit for missed days of work that is paid to the couple.
(A) 70
(B) 90
(C) 92
(D) 95
(E) 140
311. An individual experiences a loss due to property damage and a loss due to bodily injury.
Losses are independent and uniformly distributed on the interval [0, 3].
Calculate the expected loss due to bodily injury, given that at least one of the losses is
less than 1.
(A) 0.50
(B) 1.00
(C) 1.10
(D) 1.25
(E) 1.50
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312. The table below shows the joint probability function of a sailor’s number of boating
accidents and number of hospitalizations from these accidents this year.
Number of Hospitalizations from Accidents
Number of
Accidents
0 1 2 3
0 0.700
1 0.150 0.050
2 0.060 0.020 0.010
3 0.005 0.002 0.002 0.001
Calculate the sailor’s expected number of hospitalizations from boating accidents this
year.
(A) 0.085
(B) 0.099
(C) 0.410
(D) 1.000
(E) 1.500
313. Every day, the 30 employees at an auto plant each have probability 0.03 of having one
accident and zero probability of having more than one accident. Given there was an
accident, the probability of it being major is 0.01. All other accidents are minor. The
numbers and severities of employee accidents are mutually independent.
Let X and Y represent the numbers of major accidents and minor accidents, respectively,
occurring in the plant today.
Determine the joint moment generating function , ( , )X YM s t .
(A) 30
(0.01 0.02 0.97)s t
e e 
(B) 30
(0.0003 0.0297 0.97)
s t
e e 
(C) 30 30
(0.01 0.99) (0.02 0.98)s t
e e 
(D) 30 30
(0.01 0.99) (0.02 0.98)s t
e e  
(E) 30 30
(0.0003 0.9997) (0.0297 0.9703)s t
e e 
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314. The returns on two investments, X and Y, follow the joint probability density function
, 0 1
( , )
0, otherwise.
k x y
f x y
   
 

Calculate the maximum value of Var( ), 1 1Y X r r    .
(A) 1/12
(B) 1/6
(C) 1/3
(D) 2/3
(E) 1
315. On Main Street, a driver’s speed just before an accident is uniformly distributed on
[5, 20]. Given the speed, the resulting loss from the accident is exponentially distributed
with mean equal to three times the speed.
Calculate the variance of a loss due to an accident on Main Street.
(A) 525
(B) 1463
(C) 1575
(D) 1632
(E) 1744
316. Let X be the annual number of hurricanes hitting Florida, and let Y be the annual number
of hurricanes hitting Texas. X and Y are independent Poisson variables with respective
means 1.70 and 2.30.
Calculate Var( | 3)X Y X Y   .
(A) 1.71
(B) 1.77
(C) 2.93
(D) 3.14
(E) 4.00
Page 134 of 137
317. The intensity of a hurricane is a random variable that is uniformly distributed on the
interval [0, 3]. The damage from a hurricane with a given intensity y is exponentially
distributed with a mean equal to y.
Calculate the variance of the damage from a random hurricane.
(A) 1.73
(B) 1.94
(C) 3.00
(D) 3.75
(E) 6.00
318. Random variables X and Y have joint distribution
X = 0 X = 1 X = 2
Y = 0 1/15 a 2/15
Y = 1 a b a
Y = 2 2/15 a 1/15
Let a be the value that minimizes the variance of X.
Calculate the variance of Y.
(A) 2/5
(B) 8/15
(C) 16/25
(D) 2/3
(E) 7/10
319. Annual windstorm losses, X and Y, in two different regions are independent, and each is
uniformly distributed on the interval [0, 10].
Calculate the covariance of X and Y, given that X + Y < 10.
(A) –50/9
(B) –25/9
(C) 0
(D) 25/9
(E) 50/9
Page 135 of 137
320. Let X be a random variable that takes on the values –1, 0, and 1 with equal probabilities.
Let 2
Y X .
Which of the following is true?
(A) Cov(X, Y) > 0; the random variables X and Y are dependent.
(B) Cov(X, Y) > 0; the random variables X and Y are independent.
(C) Cov(X, Y) = 0; the random variables X and Y are dependent.
(D) Cov(X, Y) = 0; the random variables X and Y are independent.
(E) Cov(X, Y) < 0; the random variables X and Y are dependent.
321. Losses follow an exponential distribution with mean 1. Two independent losses are
observed.
Calculate the expected value of the smaller loss.
(A) 0.25
(B) 0.50
(C) 0.75
(D) 1.00
(E) 1.50
322. A delivery service owns two cars that consume 15 and 30 miles per gallon. Fuel costs 3
per gallon. On any given business day, each car travels a number of miles that is
independent of the other and is normally distributed with mean 25 miles and standard
deviation 3 miles.
Calculate the probability that on any given business day, the total fuel cost to the delivery
service will be less than 7.
(A) 0.13
(B) 0.23
(C) 0.29
(D) 0.38
(E) 0.47
Page 136 of 137
323. Two independent estimates are to be made on a building damaged by fire. Each estimate
is normally distributed with mean 10b and variance 2
b .
Calculate the probability that the first estimate is at least 20 percent higher than the
second.
(A) 0.023
(B) 0.100
(C) 0.115
(D) 0.221
(E) 0.444
324. The independent random variables X and Y have the same mean. The coefficients
of variation of X and Y are 3 and 4 respectively.
Calculate the coefficient of variation of
1
( )
2
X Y .
(A) 5/4
(B) 7/4
(C) 5/2
(D) 7/2
(E) 7
325. Points scored by a game participant can be modeled by Z = 3X + 2Y – 5. X and Y are
independent random variables with Var (X) = 3 and Var (Y) = 4.
Calculate Var (Z).
(A) 12
(B) 17
(C) 38
(D) 43
(E) 68
Page 137 of 137
326. An actuary is studying hurricane models. A year is classified as a high, medium, or low
hurricane year with probabilities 0.1, 0.3, and 0.6, respectively. The numbers of
hurricanes in high, medium, and low years follow Poisson distributions with means 20,
15, and 10, respectively.
Calculate the variance of the number of hurricanes in a randomly selected year.
(A) 11.25
(B) 12.50
(C) 12.94
(D) 13.42
(E) 23.75