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Question(s) / Instruction(s):

1. If the number of surface nonconformities on a specific size of metal piece is the discrete random variable in question, then the appropriate probability distribution that can describe the probability of a specific size metal sheet containing 3 nonconformities is given most likely by binomial distribution. (Points : 1)

        True

        False

 

 

 2. The _______________ describes the number of occurrences of an event over a specified interval of time or space. (Points : 1)

        Binomial random variable

        Poisson random variable

        Discrete random variable

        Continuous random variable

 

 

 3. The mean and median are the same for a normal distribution. (Points : 1)

        True

        False

 

 

 4. A random variable is said to be discrete if (Points : 1)

        its outcomes are countable.

        it can assume any real number within an interval.

        the rules of probability apply.

        it can be represented graphically.

 

 

 5. The mean of the binomial distribution is equal to (Points : 1)

        p.

        (n) (p).

        px(1-p)n-x

        (n)(p)(1-p)

 

 

 6. In a statistical study, the random variable x = 1, if the house is colonial, and x = 0 if the house is not colonial. Then it can be stated that the random variable is discrete. (Points : 1)

        True

        False

 

 

 7. Which of the following is a valid probability value for a discrete random variable? (Points : 1)

        .2   

        1.01

        -.7

        All of the above.

 

 

 8. A student's grade on an examination was transformed to a z value which is negative. Therefore, we know that he scored (Points : 1)

        higher than 16% of the class.

        higher than 45% of the class.

        above the first quartile.

        below the mean.

        above the mean but below the median.

 

 

 9. If two events are independent, then the probability of their intersection is represented by: (Points : 1)

        P(A ∩ B) = P(A) + P(B)

        P(A ∩ B) = 0

        P(A ∩ B) = P(A) * P(B)

        P(A ∩ B) = P(A) - P(B)

        P(A ∩ B) = P(A) * P(A | B)

 

 

 10. The following formula: P(A U B) = P(A) + P(B) - P(A ∩ B) represents (Points : 1)

        the conditional probability.

        the addition rule.

        independence.

        the multiplication rule.

        None of the above.

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