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1) Which of the following is not a type of probability?

A) Subjective

B) Independent

C) Relative frequency

D) Classical

2) Events are independent if

A) By virtue of one event happening another cannot.

B) The probability of their occurrence is greater than 1.

C) We can count the possible outcomes.

D) The probability of one event happening does not affect the probability of another event happening.

E) None of the above.

3) The Special Rule of Addition is used to combine

A) Independent events.

B) Mutually exclusive events

C) Events that total more than one.

D) Events based on subjective probabilities

E) Found by using joint probabilities.

4) We use the General Rule of Multiplication to combine

A) Events that are not independent.

B) Mutually exclusive events.

C) Events that total more than 1.00.

D) Events based on subjective probabilities

E) Found by using joint probabilities.

5) When we find the probability of an event happening by subtracting the probability of the event not happening from 1, we are using

A) Subjective probability

B) The complement rule.

C) The general rule of addition.

D) The special rule of multiplication

E) Joint probability

6) When we determine the number of combinations

A) We are really computing a probability.

B) The order of the outcomes is not important.

C) The order of the outcomes is important.

D) We multiple the likelihood of two independent trials.

E) None of the above.

7) Bayes' Theorem

A) Is an example of subjective probability

B) Can assume of value less than 0.

C) Is used to revise a probability based on new or additional information.

D) Is found by applying the complement rule.

E) None of the above.

8) The difference between a permutation and a combination is:

A) In a permutation order is important and in a combination it is not.

B) In a permutation order is not important and in a combination it is important.

C) A combination is based on the classical definition of probability.

D) A permutation is based on the classical definition of probability.

E) None of the above.

9) The difference between a random variable and a probability distribution is

A) A random variable does not include the probability of an event.

B) A random variable can only assume whole numbers.

C) A probability distribution can only assume whole numbers.

D) None of the above.

10) Which of the following is not a requirement of a binomial distribution?

A) A constant probability of success.

B) Only two possible outcomes.

C) A fixed number of trails.

D) Equally likely outcomes.

11) The mean and the variance are equal in

A) All probability distributions.

B) The binomial distribution.

C) The Poisson distribution.

D) The hypergeometric distribution.

12) In which of the following distributions is the probability of a success usually small?

A) Binomial

B) Poisson

C) Hypergeometric

D) All distribution

13) Which of the following is not a requirement of a probability distribution?

A) Equally likely probability of a success.

B) Sum of the possible outcomes is 1.00.

C) The outcomes are mutually exclusive.

D) The probability of each outcome is between 0 and 1.

14) For a binomial distribution

A) n must assume a number between 1 and 20 or 25.

B) ? must be a multiple of .10.

C) There must be at least 3 possible outcomes.

D) None of the above.

15) Which of the following is a major difference between the binomial and the hypergeometric distributions?

A) The sum of the outcomes can be greater than 1 for the hypergeometric.

B) The probability of a success changes from trial to trial in the hypergeometric distribution.

C) The number of trials changes in the hypergeometric distribution.

D) The outcomes cannot be whole numbers in the hypergeometric distribution.

16) In a continuous probability distribution

A) Only certain outcomes are possible.

B) All the values within a certain range are possible.

C) The sum of the outcomes is greater than 1.00

D) None of the above.

17) For a binomial distribution with n = 15 as ? changes from .50 toward .05 the distribution will

A) Become more positively skewed.

B) Become more negatively skewed

C) Become symmetrical.

D) All of the above.

18) The expected value of the a probability distribution

A) Is the same as the random variable.

B) Is another term for the mean.

C) Is also called the variance.

D) Cannot be greater than 1.

19) The normal distribution is a

A) Discrete distribution

B) Continuous distribution.

C) Positively skewed distribution

D) None of the above.

20) Which of the following are characteristics of the normal distribution?

A) It is a symmetric distribution.

B) It is bell-shaped.

C) It is asymptotic.

D) All of the above.

21) Which of the following are correct statements about a normal distribution?

A) It cannot assume negative numbers.

B) It is defined by its mean and standard deviation.

C) All normal distributions have a variance of at least 1.

D) All of the above are correct.

22) Which of the following statements is correct regarding the standard normal distribution?

A) It is also called the z distribution

B) Any normal distribution can be converted to the standard normal distribution

C) The mean is 0 and the standard deviation is 1.

D) All of the above are correct.

23) The area under a normal curve between 0 and -1.75 is

A) .0401

B) .9599

C) .4599

D) None of the above.

24) The area under a normal curve less than 1.75 is

A) .0401

B) .9599

C) .4599

D) None of the above.

25) The continuity correction factor is used when

A) The sample size is at least 5.

B) Both n? and n(1 - ?) are at least 30.

C) A continuous distribution is used to approximate a discrete distribution.

D) The standard normal distribution is applied.

26) A uniform distribution is defined by

A) Its largest and smallest value.

B) Largest value

C) Smallest value

D) None of the above.

27) The normal approximation to the binomial is used when

A) The sample size is at least 30.

B) Both n? and n(1 - ?) are at least 5.

C) The mean and the variance are the same.

D) The z value is greater than 0.

28) In the standard normal distribution, what is the probability of finding a z value between -1.25 and -1.00?

A) 0.3944

B) 0.3413

C) 0.7357

D) 0.0531

Solution Description

QNT 561