Excel’s _____ function can be used to compute the expected value of a discrete random variable.
| a. MEDIAN |
| b. SUMPRODUCT |
| c. VAR |
| d. AVERAGE |
Exhibit 5-8
The student body of a large university consists of 60% female students. A random sample of 8 students is selected.
What is the probability that among the students in the sample at least 7 are female?
| a. .0896 |
| b. .0168 |
| c. .1064 |
d. .8936 |
Exhibit 5-11
The random variable x is the number of occurrences of an event over an interval of 10 minutes. It can be assumed the probability of an occurrence is the same in any two time periods of an equal length. It is known that the mean number of occurrences in 10 minutes is 5.3.
The appropriate probability distribution for the random variable is _____.
| a. either discrete or continuous, depending on how the interval is defined |
| b. binomial |
| c. discrete |
| d. continuous |
In the textile industry, a manufacturer is interested in the number of blemishes or flaws occurring in each 100 feet of material. The probability distribution that has the greatest chance of applying to this situation is the _____.
| a. Poisson distribution |
| b. binomial distribution |
| c. uniform distribution |
d. normal distribution |
A production process produces 3% defective parts. A sample of ten parts from the production process is selected. What is the probability that the sample contains exactly three defective parts?
| a. 0.03 |
| b. 0.0270 |
| c. 0.0026 |
d. 0.30 |
The following represents the probability distribution for the daily demand of microcomputers at a local store.
| Demand | Probability |
| 0 | 0.15 |
| 1 | 0.20 |
| 2 | 0.20 |
| 3 | 0.20 |
| 4 | 0.25 |
The expected daily demand is _____.
| a. 2.2 |
| b. 1.0 |
| c. 3.2 |
| d. 4.0 |
AMR is a computer-consulting firm. The number of new clients that it has obtained each month has ranged from 0 to 6. The number of new clients has the probability distribution that is shown below.
| Number of | |
| New Clients | Probability |
| 0 | .05 |
| 1 | .10 |
| 2 | .15 |
| 3 | .35 |
| 4 | .20 |
| 5 | .10 |
| 6 | .05 |
The standard deviation is _____.
| a. 21 |
| b. 1.431 |
| c. 3.05 |
| d. 2.047 |
Experimental outcomes that are based on measurement scales such as time, weight, and distance can be described by _____ random variables.
| a. discrete |
| b. continuous |
| c. intermittent |
| d. uniform |
Which of the following is a required condition for a discrete probability function?
| a. f(x) < 0 |
| b. f(x) ≥ 1 for all values of x |
| c. Σf(x) = 0 |
| d. Σf(x) = 1 |
The expected value of a random variable is the _____.
| a. value of the random variable that occurs most frequently |
| b. value of the random variable that should be observed on the next repeat of the experiment |
| c. measure of the central location of a random variable |
d. square root of the variance |
A numerical description of the outcome of an experiment is called a _____.
| a. descriptive statistic |
| b. variance |
| c. random variable |
| d. probability function |
Excel’s HYPGEOM.DIST function has how many inputs?
| a. 4 |
| b. 5 |
| c. 3 |
d. 2 |
The number of electrical outages in a city varies from day to day. Assume that the number of electrical outages (x) in the city has the following probability distribution.
| x | f (x) |
| 0 | 0.60 |
| 1 | 0.17 |
| 2 | 0.08 |
| 3 | 0.15 |
The mean and the standard deviation for the number of electrical outages (respectively) are _____.
| a. 0.78 and 1.110 |
| b. 3 and 0.15 |
| c. 0 and 0.6 |
| d. 7.8 and 11.10 |
A marketing manager instructs his team to make 80 telephone calls to attempt to sell an insurance policy. The random variable in this experiment is the number of sales made. This random variable is a _____.
| a. binomial random variable |
| b. complex random variable |
| c. continuous random variable |
| d. discrete random variable |
Which of the following properties of a binomial experiment is called the stationarity assumption?
| a. The probability of success is the same for each trial. |
| b. The trials are independent. |
| c. Two outcomes are possible on each trial. |
| d. The experiment consists of n identical trials. |
The standard deviation is the _____.
| a. positive square root of the variance |
| b. variance squared |
| c. same as the expected value |
d. square root of the sum of the deviations from the mean |
The probability Pete will catch fish when he goes fishing is .8. Pete is going fishing 3 days next week.
The expected number of days Pete will catch fish is _____.
| a. .8 |
| b. 3 |
| c. .6 |
| d. 2.4 |
The random variable x is the number of occurrences of an event over an interval of 10 minutes. It can be assumed the probability of an occurrence is the same in any two time periods of an equal length. It is known that the mean number of occurrences in 10 minutes is 5.8.
The probability there are 8 occurrences in 10 minutes is _____.
| a. 0.0962 |
| b. 0.2290 |
| c. 0.8672 |
| d. 0.1126 |
Assume that you have a binomial experiment with p = 0.4 and a sample size of 50. The variance of this distribution is _____.
| a. 20 |
| b. 12.5 |
| c. 3.46 |
d. 12 |
Exhibit 5-8
The student body of a large university consists of 60% female students. A random sample of 8 students is selected.
What is the probability that among the students in the sample at least 6 are male?
| a. .0413 |
| b. .0499 |
| c. .0079 |
| d. .0007 |
Exhibit 5-5
AMR is a computer-consulting firm. The number of new clients that it has obtained each month has ranged from 0 to 6. The number of new clients has the probability distribution that is shown below.
| Number of | |
| New Clients | Probability |
| 0 | .05 |
| 1 | .10 |
| 2 | .15 |
| 3 | .35 |
| 4 | .20 |
| 5 | .10 |
| 6 | .05 |
The variance is _____.
| a. 3.05 |
| b. 2.0475 |
| c. 21 |
| d. 1.431 |
Exhibit 5-8
The student body of a large university consists of 60% female students. A random sample of 8 students is selected.
What is the probability that among the students in the sample exactly two are female?
| a. .0896 |
| b. .0413 |
| c. .0007 |
d. .2936 |
Exhibit 5-10
The probability Pete will catch fish when he goes fishing is .8. Pete is going fishing 3 days next week.
What is the random variable in this experiment?
| a. the .8 probability of catching fish |
| b. the number of days out of 3 that Pete catches fish |
| c. the 3 days |
| d. the number of fish in the body of water |
The variance for the binomial probability distribution is _____.
| a. Var(x) = np(1 − p) |
| b. Var(x) = np |
| c. Var(x) = n(1 − p) |
d. Var(x) = p(1 − p) |
AMR is a computer-consulting firm. The number of new clients that it has obtained each month has ranged from 0 to 6. The number of new clients has the probability distribution that is shown below.
| Number of | |
| New Clients | Probability |
| 0 | .05 |
| 1 | .10 |
| 2 | .15 |
| 3 | .35 |
| 4 | .20 |
| 5 | .10 |
| 6 | .05 |
The expected number of new clients per month is _____.
| a. 0 |
| b. 6 |
| c. 21 |
| d. 3.05 |
The variance is a weighted average of the _____.
| a. square root of the deviations from the mean |
| b. squared deviations from the mean |
| c. squared deviations from the median |
| d. square root of the deviations from the median |
The random variable x is the number of occurrences of an event over an interval of 10 minutes. It can be assumed the probability of an occurrence is the same in any two time periods of an equal length. It is known that the mean number of occurrences in 10 minutes is 5.3.
The probability there are less than 3 occurrences is _____.
| a. .1239 |
| b. .1016 |
| c. .0659 |
d. .0948 |
The probability distribution for the number of goals the Lions soccer team makes per game is given below.
| Number of Goals | Probability |
| 0 | 0.15 |
| 1 | 0.35 |
| 2 | 0.10 |
| 3 | 0.10 |
| 4 | 0.30 |
What is the probability that in a given game the Lions will score at least 1 goal?
| a. 1.0 |
| b. 0.15 |
| c. 0.85 |
| d. 0.50 |
local bottling company has determined the number of machine breakdowns per month and their respective probabilities as shown below.
| Number of | |
| Breakdowns | Probability |
| 0 | .12 |
| 1 | .38 |
| 2 | .25 |
| 3 | .18 |
| 4 | .07 |
The probability of at least 3 breakdowns in a month is _____.
| a. .30 |
| b. .10 |
| c. .5 |
d. .25 |
Which of the following is NOT a characteristic of an experiment where the binomial probability distribution is applicable?
| a. The trials are dependent. |
| b. Exactly two outcomes are possible on each trial. |
| c. The probabilities of the outcomes do not change from one trial to another. |
| d. The experiment has a sequence of n identical trials. |
Which of the following is NOT a required condition for a discrete probability function?
| a. Σf(x) = 1 |
| b. Σf(x) = 0 |
| c. f(x) ≥ 0 for all values of x |
| d. There are no required conditions for a discrete probability function. |
Exhibit 5-11
The random variable x is the number of occurrences of an event over an interval of 10 minutes. It can be assumed the probability of an occurrence is the same in any two time periods of an equal length. It is known that the mean number of occurrences in 10 minutes is 5.3.
The probability there are 8 occurrences in 10 minutes is _____.
| a. .9107 |
| b. .0241 |
| c. .1126 |
| d. .0771 |
The probability Pete will catch fish when he goes fishing is 0.7. Pete is going fishing 4 days next week.
The probability that Pete will catch fish on 1 or fewer days is _____.
| a. 0.076 |
| b. 0.008 |
| c. 0.70 |
| d. 0.084 |
Which of the following is a characteristic of a binomial experiment?
| a. At least two outcomes are possible. |
| b. The probability of success changes from trial to trial. |
| c. The experiment consists of a sequence of different trials. |
| d. The trials are independent. |
The weight of an object, measured to the nearest gram, is an example of _____.
| a. a continuous random variable |
| b. a discrete random variable |
| c. either a continuous or a discrete random variable, depending on the weight of the object |
| d. either a continuous or a discrete random variable, depending on the units of measurement |
If you are conducting an experiment where the probability of a success is .02 and you are interested in the probability of two successes in 15 trials, the correct probability function to use is the _____.
| a. Poisson probability function |
| b. binomial probability function |
| c. normal probability density function |
d. standard normal probability density function |
Exhibit 5-11
The random variable x is the number of occurrences of an event over an interval of 10 minutes. It can be assumed the probability of an occurrence is the same in any two time periods of an equal length. It is known that the mean number of occurrences in 10 minutes is 5.3.
The appropriate probability distribution for the random variable is _____.
| a. discrete |
| b. binomial |
| c. either discrete or continuous, depending on how the interval is defined |
d. continuous |
In a binomial experiment, the probability of success is .06. What is the probability of two successes in seven trials?
| a. .28 |
| b. .0036 |
| c. .0554 |
d. .06 |
Assume that you have a binomial experiment with p = 0.3 and a sample size of 100. The expected value of this distribution is _____.
| a. 30 |
| b. 0.70 |
| c. 0.09 |
d. 0.30 |
When using Excel’s POISSON.DIST function, one should choose TRUE for the third input if _____.
| a. the expected value is desired |
| b. a probability is desired |
| c. the correct answer is desired |
| d. a cumulative probability is desired |
The expected value of a discrete random variable _____.
| a. is the most likely or highest probability value for the random variable |
| b. is the average value for the random variable over many repeats of the experiment |
| c. will always be one of the values x can take on, although it may not be the highest probability value for the random variable |
d. cannot be calculated using Excel. |
A sample of 2,500 people was asked how many cups of coffee they drink in the morning. You are given the following sample information.
| Cups of Coffee | Frequency | |
| 0 | 700 | |
| 1 | 900 | |
| 2 | 600 | |
| 3 | 300 | |
| 2,500 | ||
The expected number of cups of coffee is _____.
| a. 1.2 |
| b. 1.5 |
| c. 1.7 |
| d. 1 |
When dealing with the number of occurrences of an event over a specified interval of time or space and when the occurrence or nonoccurrence in any interval is independent of the occurrence or nonoccurrence in any other interval, the appropriate probability distribution is a _____.
| a. hypergeometric probability distribution |
| b. binomial distribution |
| c. normal distribution |
| d. Poisson distribution |
AMR is a computer-consulting firm. The number of new clients that it has obtained each month has ranged from 0 to 6. The number of new clients has the probability distribution that is shown below.
| Number of | |
| New Clients | Probability |
| 0 | 0.15 |
| 1 | 0.10 |
| 2 | 0.30 |
| 3 | 0.10 |
| 4 | 0.25 |
| 5 | 0.05 |
| 6 | 0.05 |
The expected number of new clients per month is _____.
| a. 21 |
| b. 2.55 |
| c. 2 |
| d. 3.55 |
To compute a binomial probability. we must know all of the following except the _____.
| a. number of trials |
| b. probability of success |
| c. number of elements in the population |
| d. value of the random variable |
Exhibit 5-11
The random variable x is the number of occurrences of an event over an interval of 10 minutes. It can be assumed the probability of an occurrence is the same in any two time periods of an equal length. It is known that the mean number of occurrences in 10 minutes is 5.3.
The expected value of the random variable x is _____.
| a. 2 |
| b. 5.3 |
| c. 10 |
d. 2.30 |
Variance is _____.
| a. a measure of the average, or central value of a random variable |
| b. the sum of the deviation of data elements from the mean |
| c. a measure of the dispersion of a random variable |
| d. the square root of the standard deviation |
A production process produces 2% defective parts. A sample of five parts from the production process is selected. What is the probability that the sample contains exactly two defective parts?
| a. .0038 |
| b. .10 |
| c. .0004 |
d. .02 |
Excel’s EXPON.DIST function has how many inputs?
| a. 4 |
| b. 3 |
| c. 2 |
| d. 5 |
If arrivals follow a Poisson probability distribution, the time between successive arrivals must follow a(n) _____ probability distribution.
| a. exponential |
| b. normal |
| c. Poisson |
| d. uniform |
Excel’s NORM.INV function can be used to compute _____.
| a. the normally distributed x value given a cumulative probability |
| b. cumulative probabilities for a standard normal z value |
| c. the standard normal z value given a cumulative probability |
| d. cumulative probabilities for a normally distributed x value |
Assume z is a standard normal random variable. Then P(1.20 ≤ z ≤ 1.85) equals _____.
| a. .8527 |
| b. .3849 |
| c. .0829 |
d. .4678 |
Excel’s NORM.S.INV function can be used to compute _____.
| a. cumulative probabilities for a normally distributed x value |
| b. cumulative probabilities for a standard normal z value |
| c. the normally distributed x value given a cumulative probability |
d. the standard normal z value given a cumulative probability |
Consider the following.
f(x) = (1/5) e–x/5x ≥ 0
The mean of x is _____.
| a. 5 |
| b. 25 |
| c. 0.5 |
| d. 1/5 |
Consider the continuous random variable x, which has a uniform distribution over the interval from 40 to 42. The probability density function has what value in the interval between 40 and 42?
| a. 0 |
| b. 0.5 |
| c. 1 |
| d. 2 |
For a standard normal distribution, a negative value of z indicates _____.
| a. the z is to the left of the mean |
| b. the area corresponding to the z is negative |
| c. a mistake has been made in computations, because z is always positive |
| d. the z is to the right of the mean |
Assume z is a standard normal random variable. Then P(–1.96 ≤ z ≤ –1.2) equals _____.
| a. 0.4750 |
| b. 0.0901 |
| c. 0.8599 |
d. 0.3849 |
Exhibit 6-5
The weight of items produced by a machine is normally distributed with a mean of 8 ounces and a standard deviation of 2 ounces.
Refer to Exhibit 6-5. What is the probability that a randomly selected item weighs exactly 8 ounces?
| a. 0 |
| b. 1.0 |
| c. .3413 |
| d. .5 |
The life expectancy of a particular brand of tire is normally distributed with a mean of 30,000 and a standard deviation of 4,000 miles. What is the probability that a randomly selected tire will have a life of at least 35,000 miles?
| a. 0.3944 |
| b. 0.8944 |
| c. 0.6056 |
| d. 0.1056 |
The skewness measure for exponential distributions is _____.
| a. 3 |
| b. 1 |
| c. 0 |
| d. 2 |
Assume z is a standard normal random variable. Then P(–1.4 ≤ z ≤ 1.05) equals _____.
| a. 0.4192 |
| b. 0.3531 |
| c. 0.0661 |
| d. 0.7724 |
Whenever the probability is proportional to the length of the interval in which the random variable can assume a value, the random variable follows a(n) _____ distribution.
| a. exponential |
| b. Poisson |
| c. normal |
| d. uniform |
Assume z is a standard normal random variable. Then P(z ≥ 2.22) equals _____.
| a. 0.0132 |
| b. 0.50 |
| c. 0.4868 |
| d. 0.9868 |
The uniform probability distribution is used with _____.
| a. a normally distributed random variable |
| b. any random variable |
| c. a discrete random variable |
| d. a continuous random variable |
Suppose x is a normally distributed random variable with a mean of 21 and a standard deviation of 5. The probability that x is less than 8.8 is _____.
| a. 0.4927 |
| b. 0.0073 |
| c. 0 |
d. 0.9927 |
Consider the continuous random variable x, which has a uniform distribution over the interval from 50 to 55. The probability density function has what value in the interval between 50 and 55?
| a. 1 |
| b. 0.2 |
| c. 0 |
d. 5 |
The probability density function for a uniform distribution ranging between 2 and 6 is _____.
| a. 4 |
| b. any positive value |
| c. undefined |
d. .25 |
For a standard normal distribution, the probability of obtaining a z value between –2.6 and –2.0 is _____.
| a. 0.0455 |
| b. 0.0274 |
| c. 0.0181 |
| d. 0.0093 |
For a continuous random variable x, the probability density function f(x) represents _____.
| a. the distribution of a given value of x |
| b. the area under the curve at x |
| c. the height of the function at x |
d. the probability at a given value of x |
A continuous random variable may assume _____.
| a. all values in an interval or collection of intervals |
| b. only integer values in an interval or collection of intervals |
| c. only fractional values in an interval or collection of intervals |
| d. all the positive integer values in an interval |
The random variable x is known to be uniformly distributed between 50 and 80. The probability of x having a value between 65 and 85 is _____.
| a. 0.5 |
| b. 0.75 |
| c. 0.05 |
d. 1 |
Consider the following.
f(x) = (1/3) e–x/3x ≥ 0
The mean of x is _____.
| a. 3 |
| b. 0.3 |
| c. 1/3 |
d. 9 |
For a standard normal distribution, the probability of obtaining a z value of less than 1.8 is _____.
| a. 0.0359 |
| b. 0.9641 |
| c. 0.4641 |
| d. 0.9281 |
The weight of items produced by a machine is normally distributed with a mean of 9 ounces and a standard deviation of 3 ounces. What is the probability that a randomly selected item will weigh between 14 and 16 ounces?
| a. 0.4902 |
| b. 0.4522 |
| c. 0.9240 |
d. 0.0380 |
Consider the continuous random variable x, which has a uniform distribution over the interval from 10 to 14. The probability that x will take on a value of at least 13 is _____.
| a. 0.75 |
| b. 0.25 |
| c. 1 |
d. 0 |
Larger values of the standard deviation result in a normal curve that is _____.
| a. shifted to the right |
| b. wider and flatter |
| c. narrower and more peaked |
d. shifted to the left |
Excel’s NORM.S.DIST function can be used to compute _____.
| a. cumulative probabilities for a standard normal z value |
| b. the normally distributed x value given a cumulative probability |
| c. the standard normal z value given a cumulative probability |
d. cumulative probabilities for a normally distributed x value |
What type of function defines the probability distribution of ANY continuous random variable?
| a. Uniform distribution function |
| b. Normal distribution function |
| c. Exponential distribution function |
d. Probability density function |
The starting salaries of individuals with an MBA degree are normally distributed with a mean of $45,000 and a standard deviation of $8,000. What percentage of MBAs will have starting salaries of $34,000 to $56,000?
| a. 83.09% |
| b. 50% |
| c. 41.54% |
| d. 33.09% |
The exponential probability distribution is used with _____.
| a. an approximation of the binomial probability distribution |
| b. a continuous random variable |
| c. a discrete random variable |
| d. any probability distribution with an exponential term |
Assume z is a standard normal random variable. Then P(1.41 < z < 2.85) equals _____.
| a. .0771 |
| b. .4772 |
| c. .3413 |
d. .8285 |
Suppose x is a normally distributed random variable with a mean of 5 and a variance of 4. The probability that x is greater than 10.52 is _____.
| a. .0029 |
| b. .4971 |
| c. .0838 |
| d. .9971 |
A continuous random variable may assume _____.
| a. all values in an interval or collection of intervals |
| b. all the positive integer values in an interval |
| c. only fractional values in an interval or collection of intervals |
| d. only integer values in an interval or collection of intervals |
Given that z is a standard normal random variable, what is the value of z if the area to the right of z is 0.0694?
| a. 1.82 |
| b. 0.4723 |
| c. 1.48 |
| d. –1.48 |
The starting salaries of individuals with an MBA degree are normally distributed with a mean of $50,000 and a standard deviation of $5,000. What percentage of MBAs will have starting salaries of $42,000 to $58,000?
| a. 39.04% |
| b. 50% |
| c. 44.52% |
| d. 89.04% |
The assembly time for a product is uniformly distributed between 7 and 12 minutes. The probability of assembling the product in 11 minutes or more is _____.
| a. 0.2 |
| b. 1 |
| c. 0.8 |
d. 0 |
Exhibit 6-1
Consider the continuous random variable x, which has a uniform distribution over the interval from 20 to 28.
Refer to Exhibit 6-1. The variance of x is approximately _____.
| a. 5.333 |
| b. 32 |
| c. 2.309 |
| d. .667 |
The weight of items produced by a machine is normally distributed with a mean of 9 ounces and a standard deviation of 3 ounces. What percentage of items will weigh at least 12.15 ounces?
| a. 64.69% |
| b. 14.69% |
| c. 35.31% |
| d. 85.31% |
If the mean of a normal distribution is negative, _____.
| a. the variance must also be negative |
| b. the median and mode must also be negative |
| c. a mistake has been made in the computations, because the mean of a normal distribution cannot be negative |
| d. the standard deviation must also be negative |
The starting salaries of individuals with an MBA degree are normally distributed with a mean of $45,000 and a standard deviation of $8,000. What is the probability that a randomly selected individual with an MBA degree will get a starting salary of at least $59,400?
| a. 0.4641 |
| b. 0.9641 |
| c. 0.0359 |
| d. .5000 |
Larger values of the standard deviation result in a normal curve that is _____.
| a. narrower and more peaked |
| b. wider and flatter |
| c. shifted to the left |
d. shifted to the right |
The random variable x is known to be uniformly distributed between 70 and 90. The probability of x having a value between 80 and 95 is _____.
| a. .5 |
| b. .75 |
| c. .05 |
| d. 1 |
Suppose x is a normally distributed random variable with a mean of 5 and a variance of 4. The probability that x is greater than 10.52 is _____.
| a. .0029 |
| b. .0838 |
| c. .4971 |
d. .9971 |
The exponential probability distribution is used with _____.
| a. an approximation of the binomial probability distribution |
| b. any probability distribution with an exponential term |
| c. a discrete random variable |
| d. a continuous random variable |
The form of the continuous uniform probability distribution is _____.
| a. triangular |
| b. a series of vertical lines |
| c. bell-shaped |
| d. rectangular |
An exponential probability distribution _____.
| a. must be normally distributed |
| b. is a continuous distribution |
| c. must be uniformly distributed |
| d. is a discrete distribution |
Exhibit 6-1
Consider the continuous random variable x, which has a uniform distribution over the interval from 20 to 28.
Refer to Exhibit 6-1. The probability density function has what value in the interval between 20 and 28?
| a. 1 |
| b. 0 |
| c. .125 |
| d. .050 |
The assembly time for a product is uniformly distributed between 6 and 11 minutes. The probability density function has what value in the interval between 6 and 11?
| a. 5.00 |
| b. 0.20 |
| c. 0 |
| d. 6.00 |
The skewness measure for exponential distributions is _____.
| a. 1 |
| b. 3 |
| c. 0 |
| d. 2 |
Assume z is a standard normal random variable. What is the value of z if the area between –z and z is 0.8611?
| a. 0.097 |
| b. 1.085 |
| c. 1.48 |
| d. 0.17 |
Exhibit 6-7
f(x) = (1/10) e–x/10x ≥ 0
Refer to Exhibit 6-7. The mean of x is _____.
| a. 10 |
| b. 1,000 |
| c. .10 |
| d. 100 |
Exhibit 6-2
The travel time for a college student traveling between her home and her college is uniformly distributed between 40 and 90 minutes.
Refer to Exhibit 6-2. What is the random variable in this experiment?
| a. 40 minutes |
| b. Distance from home to college |
| c. Travel time |
| d. 90 minutes |
The weight of football players is normally distributed with a mean of 215 pounds and a standard deviation of 20 pounds. The probability of a player weighing less than 265 pounds is _____.
| a. 0.9938 |
| b. 0.4938 |
| c. .5000 |
| d. 0.0062 |
The probability distribution that can be described by just one parameter is the _____ distribution.
| a. normal |
| b. uniform |
| c. exponential |
| d. continuous |
The skewness measure for exponential distributions is _____.
| a. 0 |
| b. 2 |
| c. 3 |
| d. 1 |
Exhibit 6-2
The travel time for a college student traveling between her home and her college is uniformly distributed between 40 and 90 minutes.
Refer to Exhibit 6-2. What is the random variable in this experiment?
| a. 40 minutes |
| b. Distance from home to college |
| c. Travel time |
d. 90 minutes |
A property of the exponential distribution is that the mean equals the _____.
| a. mode |
| b. standard deviation |
| c. median |
d. variance |
The assembly time for a product is uniformly distributed between 6 and 10 minutes. The probability density function has what value in the interval between 6 and 10?
| a. 5.00 |
| b. 4.00 |
| c. 0 |
| d. .25 |
About 95.4% of the values of a normal random variable are within approximately how many standard deviations of its mean?
| a. ±3 |
| b. ±1.7 |
| c. ±2.5 |
d. ±2 |
The ages of students at a university are normally distributed with a mean of 20. What percentage of the student body is at least 20 years old?
| a. 20% |
| b. It could be any value, depending on the magnitude of the standard deviation. |
| c. 1.96% |
| d. 50% |
Excel’s NORM.S.INV function can be used to compute _____.
| a. cumulative probabilities for a normally distributed x value |
| b. cumulative probabilities for a standard normal z value |
| c. the standard normal z value given a cumulative probability |
d. the normally distributed x value given a cumulative probability |
A continuous random variable is uniformly distributed between a and b. The probability density function between a and b is _____.
| a. (b − a) |
| b. (a − b) |
| c. 0 |
d. 1/(b − a) |
Exhibit 6-5
The weight of items produced by a machine is normally distributed with a mean of 8 ounces and a standard deviation of 2 ounces.
Refer to Exhibit 6-5. What percentage of items will weigh at least 11.7 ounces?
| a. 96.78% |
| b. 3.22% |
| c. 46.78% |
| d. 53.22% |
The ages of students at a university are normally distributed with a mean of 19. What percentage of the student body is at least 19 years old?
| a. 50% |
| b. It could be any value, depending on the magnitude of the standard deviation. |
| c. 19% |
| d. 1.96% |
The travel time for a college student traveling between her home and her college is uniformly distributed between 30 and 80 minutes. The probability that her trip will take exactly 50 minutes is _____.
| a. 0.4000 |
| b. 0.9800 |
| c. 0 |
| d. 0.0200 |
If the mean of a normal distribution is negative, _____.
| a. the median and mode must also be negative |
| b. the variance must also be negative |
| c. a mistake has been made in the computations, because the mean of a normal distribution cannot be negative |
d. the standard deviation must also be negative |
All of the following distributions are symmetric EXCEPT the _____ distribution.
| a. normal |
| b. exponential |
| c. uniform |
d. standard normal |
Which of the following is NOT a characteristic of the normal probability distribution?
| a. The random variable assumes a value within plus or minus three standard deviations of its mean 99.72% of the time. |
| b. The mean is equal to the median, which is also equal to the mode. |
| c. The total area under the curve is always equal to 1. |
| d. The graph of the curve is the shape of a rectangl |
For a standard normal distribution, the probability of z ≤ 0 is _____.
| a. 0 |
| b. 1 |
| c. –.5 |
| d. .5 |
Exhibit 6-3
The weight of football players is normally distributed with a mean of 200 pounds and a standard deviation of 25 pounds.
Refer to Exhibit 6-3. The probability of a player weighing more than 241.25 pounds is _____.
| a. .9505 |
| b. .9010 |
| c. .4505 |
d. .0495 |
The mean, median, and mode have the same value for which of the following probability distributions?
| a. normal |
| b. exponential |
| c. Poisson |
| d. uniform |
The assembly time for a product is uniformly distributed between 5 and 10 minutes. The probability of assembling the product in 6 to 9 minutes is _____.
| a. 1 |
| b. 0.6 |
| c. 0.4 |
| d. 0 |
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