Ch. 5 Check Your Knowledge
In probability theory, we can use the multiplication law to calculate the probability of
the intersection of two events
A probability tree is used in probability theory to compute
joint probabilities and conditional probabilities
A random variable representing the number of occurrences of an event, which follows a Poisson probability distribution is characterized by
two properties of equal probability of occurrence for any two intervals of equal length and independence of occurrences in different intervals are satisfied.
A uniform probability distribution is the one in which
every interval of a given length is equally likely
Which of the following best describes a random variable in probability terms?
A quantity whose value is NOT known with certainty and represents the outcome of a random experiment
Which of the following best describes a binomial probability distribution?
A discrete probability distribution that can be used to describe many situations in which a fixed number of repeated identical and independent trials has two, and only two, possible outcomes
Which of the following uses the correct notation to represent the union of events A and B?
A ∪ B
Which of the following best describes conditional probability and its application in risk assessment of home mortgage customers?
Conditional probability is the probability of an event A occurring given the occurrence of another related event B.
Which of the following statements is true about dependent and independent events?
If P(D | M) is equal to P(D), then events D and M are independent.
Which values are needed to specify the triangular probability distribution?
Minimum possible value, maximum possible value, and mode
Which of the following best shows the relationship between the probability of an event, A, and its complement, AC?
P(A) = 1 - P(AC)
Which of the following results from revising prior probability values in probability analysis?
Posterior probabilities
Which of the following mathematical operations is used to express when the probability of an event is equal to the probabilities of the individual outcomes in a random experiment?
Sum
What is the difference between a probability mass function and a probability density function?
The probability mass function directly provides probabilities, whereas the probability density function does not.
Why are probabilities revised in probability analysis?
To incorporate new information into the prior probability estimates for specific events
Which of the following best describes computing a Poisson probability for a different time interval?
We must first convert the mean arrival rate to the period of interest and then use the POISSON.DIST function in Excel.
Which of the following is NOT true about conditional probability?
b. P(A | B) is the probability of event A given that event B has already occurred. c. P(A) = P(A | B) d. P(A | B) is independent of event B. DONT KNOW THE RIGHT ANSWER
Financial values, such as amounts, are categorized as
both a discrete and a continuous random variable, depending on the context
The probability of the intersection of two events is called a(n)
joint probability
A continuous random variable is a random variable that
may assume any numerical value in an interval or collection of intervals
In probability analysis, Bayes' theorem is applicable when events to compute posterior probabilities are
mutually exclusive and their union is the entire sample space
A discrete random variable is a random variable that can take on
only specified discrete values
A triangular probability distribution is useful when
only subjective probability estimates are available
The area under the curve corresponding to an interval in a continuous probability distribution provides the
probability that the random variable assumes a value in that interval
The probability for a continuous random variable is the likelihood that a random variable assumes a
value within a specified interval
A variance in probability and statistics is described as a measure of
variability in the values of a random variable
A random experiment is defined as a process that generates
well-defined outcomes
