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Question 1 of 10
1. Question
The binomial distribution is appropriate to use in what type of conditions when using it for modeling the number of claims?
Correct
The binomial family of distributions has two parameters, n, and p,n being the positive integer. The distribution models the number of successes that occur in a series of independent Bernoulli trials, each with success and failure. Unlike the other distributions, the values which can be assumed by the binomial random variable are restricted – to a maximum of numbers of claims that is why this distribution is only appropriate in situations in which we know in advance the maximum possible number of claims.When the maximum possible number of successes are known in advance.
Incorrect
The binomial family of distributions has two parameters, n, and p,n being the positive integer. The distribution models the number of successes that occur in a series of independent Bernoulli trials, each with success and failure. Unlike the other distributions, the values which can be assumed by the binomial random variable are restricted – to a maximum of numbers of claims that is why this distribution is only appropriate in situations in which we know in advance the maximum possible number of claims.When the maximum possible number of successes are known in advance.
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Question 2 of 10
2. Question
What is an insurance loss?
Correct
When an insured event occurs, the cost to the insurer is known as an insurance loss.
Incorrect
When an insured event occurs, the cost to the insurer is known as an insurance loss.
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Question 3 of 10
3. Question
What are the loss distributions?
Correct
Loss distributions are the distributions used to model the costs of an insurance loss, an insurance loss being the cost suffered by the insurer when an insured event occurs.
Incorrect
Loss distributions are the distributions used to model the costs of an insurance loss, an insurance loss being the cost suffered by the insurer when an insured event occurs.
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Question 4 of 10
4. Question
Out of the following, which one of these is an example for the series of positive distributions, that is also regarded as being fat-tailed?
Correct
these are the examples for the series of positive distributions, that are also regarded as being fat-tailed:
• lognormal distribution.
• Pareto distribution.
• Weibull distribution.
• Burr distribution.
• log gamma.Incorrect
these are the examples for the series of positive distributions, that are also regarded as being fat-tailed:
• lognormal distribution.
• Pareto distribution.
• Weibull distribution.
• Burr distribution.
• log gamma. -
Question 5 of 10
5. Question
Which one of these is true about the corresponding commands for the Burr, log gamma, and Pareto distributions?
Correct
The commands for the Burr, log gamma, and Pareto distributions are not supported in the basic R package and are made available in an add-on in
the package called actuar.Incorrect
The commands for the Burr, log gamma, and Pareto distributions are not supported in the basic R package and are made available in an add-on in
the package called actuar. -
Question 6 of 10
6. Question
What does the basic R provide for the gamma, lognormal, normal, and Weibull distributions?
Correct
The basic R package is a provider for specific commands in order to calculate the probability density function, the distribution function, and quantiles for the gamma, lognormal, normal, and Weibull distributions, along with
commands to simulate observations from them.Incorrect
The basic R package is a provider for specific commands in order to calculate the probability density function, the distribution function, and quantiles for the gamma, lognormal, normal, and Weibull distributions, along with
commands to simulate observations from them. -
Question 7 of 10
7. Question
Why is the flexibility of the exponential family as a model for data restricted?
Correct
The flexibility of the exponential family when used as a model for data is restricted due to the fact that it has only one parameter. The exponential family has a single parameter, which is usually denoted as λ (> 0).
Incorrect
The flexibility of the exponential family when used as a model for data is restricted due to the fact that it has only one parameter. The exponential family has a single parameter, which is usually denoted as λ (> 0).
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Question 8 of 10
8. Question
The exponential distribution possesses an important factor which is denoted by X ∼ Exp(λ) ⇒ X − w | X > w ∼ Exp(λ), what is the term for that important factor?
Correct
The exponential distribution possesses an important property known as the lack of memory, which is often useful in certain reinsurance calculations, often denoted by X ∼ Exp(λ) ⇒ X − w | X > w ∼ Exp(λ).
Incorrect
The exponential distribution possesses an important property known as the lack of memory, which is often useful in certain reinsurance calculations, often denoted by X ∼ Exp(λ) ⇒ X − w | X > w ∼ Exp(λ).
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Question 9 of 10
9. Question
What do you understand by these lines ≡ when denoted in the formula for the modeling of the distribution of the claims?
Correct
≡ means “has the same distribution as” when used in the formula as X − w | X > w ≡ X, for expressing the lack of memory in the exponential distribution.
Incorrect
≡ means “has the same distribution as” when used in the formula as X − w | X > w ≡ X, for expressing the lack of memory in the exponential distribution.
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Question 10 of 10
10. Question
Which one of these sentences correctly describes the given definition of a memory loss in the exponential distribution?
Correct
The lack of memory property of the exponential distribution means that the distribution “does not remember” that time w has already elapsed when given that a time w has already elapsed since the last event, the probability that a further time x elapses before the next event is simply the original unconditional probability that the inter-event time is at least x times.
Incorrect
The lack of memory property of the exponential distribution means that the distribution “does not remember” that time w has already elapsed when given that a time w has already elapsed since the last event, the probability that a further time x elapses before the next event is simply the original unconditional probability that the inter-event time is at least x times.