1.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) uniform
2.There is a lower limit but no upper limit for a random variable that follows the _____ probability distribution.
A) exponential
3.The form of the continuous uniform probability distribution is _____.
A) rectangular
4.The mean, median, and mode have the same value for which of the following probability distributions?
A) Normal
5.The probability distribution that can be described by just one parameter is the _____ distribution.
A) exponential
6.A continuous random variable may assume _____.
A) all values in an interval or collection of intervals
7.For a continuous random variable x, the probability density function f(x) represents _____.
A) the height of the function at x
8.What type of function defines the probability distribution of ANY continuous random variable?
A) Probability density function
9.For any continuous random variable, the probability that the random variable takes on exactly a specific value is _____.
A) 0
10.The uniform probability distribution is used with _____.
A) a continuous random variable
11.A uniform probability distribution is a continuous probability distribution where the probability that the random variable assumes a value in any interval of equal length is _____.
A) the same for each interval
12.For a uniform probability density function, the height of the function _____.
A) is the same for each value of x
13.A continuous random variable is uniformly distributed between a and b. The probability density function between a and b is _____.
A) 1(a-b)
14.The probability density function for a uniform distribution ranging between 2 and 6 is _____.
A) 25
15.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) 0.5
16.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) 0.25
17.The assembly time for a product is uniformly distributed between 6 and 10 minutes. The probability of assembling the product between 7 and 9 minutes is _____.
A) 0.50
18.The assembly time for a product is uniformly distributed between 6 and 10 minutes. The probability of assembling the product in less than 6 minutes is _____.
A) 0
19.The assembly time for a product is uniformly distributed between 6 and 10 minutes. The probability of assembling the product in 7 minutes or more is _____.
A) 0.75
20.The assembly time for a product is uniformly distributed between 6 and 10 minutes. The expected assembly time (in minutes) is _____.
A) 8
21.A normal probability distribution _____.
A) is a continuous probability distribution
22.Which of the following is NOT a characteristic of the normal probability distribution?
A) The mean, median, and mode are equal.
23.Which of the following is NOT a characteristic of the normal probability distribution?
A) The total area under the curve is always equal to 1.
24.The highest point of a normal curve occurs at _____.
A) The Mean
25.Larger values of the standard deviation result in a normal curve that is _____.
A) wider and flater
26.A standard normal distribution is a normal distribution with _____.
A) a mean of 0 and a standard deviation of 1
27.In a standard normal distribution, the range of values of z is from _____.
A) minus infinity to infinity
28.For a standard normal distribution, a negative value of z indicates _____.
A) the z is to the left of the mean
29.For the standard normal probability distribution, the area to the left of the mean is _____.
A) 0.5
30.The standard deviation of a standard normal distribution _____.
A) is always equal to 1
31.For a standard normal distribution, the probability of z ≤ 0 is _____.
A) 0.5
32.Assume z is a standard normal random variable. Then P(1.20 ≤ z ≤ 1.85) equals _____.
A) 0.0829
33.Assume z is a standard normal random variable. Then P(-1.96 ≤ z ≤ -1.4) equals _____.
A) 0.0558
34.Assume z is a standard normal random variable. Then P(-1.20 ≤ z ≤ 1.50) equals _____.
A) 0.8181
35.Assume z is a standard normal random variable. Then P(-1.5 ≤ z ≤ 1.09) equals _____.
A) 0.7953
36.Assume z is a standard normal random variable. Then P(z ≥ 2.11) equals _____.
A) 0.0174
37.Given that z is a standard normal random variable, what is the value of z if the area to the right of z is 0.1112?
A) 1.22
38.Given that z is a standard normal random variable, what is the value of z if the area to the right of z is 0.1401?
A) 1.08
39.Given that z is a standard normal random variable, what is the value of z if the area to the left of z is 0.9382?
A) 1.54
40.Assume z is a standard normal random variable. What is the value of z if the area between -z and z is 0.754?
A) 1.16
41.Assume z is a standard normal random variable. What is the value of z if the area to the right of z is 0.9803?
A) -2.06
42.For a standard normal distribution, the probability of obtaining a z value between -2.4 and -2.0 is _____.
A) 0.0146
43.For a standard normal distribution, the probability of obtaining a z value of less than 1.6 is _____.
A) 0.9452
44.7450. For a standard normal distribution, the probability of obtaining a z value between -1.9 and 1.7 is _____.
A) 0.9267
45.Suppose x is a normally distributed random variable with a mean of 8 and a standard deviation of 4. The probability that x is between 1.48 and 15.56 is _____.
A) 0.9190
46.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) 0.0029
47.Suppose x is a normally distributed random variable with a mean of 12 and a standard deviation of 3. The probability that x equals 19.62 is _____.
A) 0
48.Suppose x is a normally distributed random variable with a mean of 22 and a standard deviation of 5. The probability that x is less than 9.7 is _____.
A) 0.0069
49.The ages of students at a university are normally distributed with a mean of 21. What percentage of the student body is at least 21 years old?
A) 50%
50. 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) 0.125
51.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 that x will take on a value between 21 and 25 is_____. A) 0.5
52.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 that x will take on a value of at least 26 is _____. A) 0.250
53.Consider the continuous random variable x, which has a uniform distribution over the interval from 20 to 28. Refer to Exhibit 6-1. The mean of x is _____.
A) 24
54.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
55.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) time travel
56.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. The probability that she will finish her trip in 80 minutes or less is _____. A) 0.8
57.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. The probability that her trip will take longer than 60 minutes is _____. A) 0.60
58.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. The probability that her trip will take exactly 50 minutes is _____. A) 0
59. 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. What is the random variable in this experiment?
A) Weight of football players
60.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) 0.0495
61.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 less than 250 pounds is _____. A) 0.9772
62.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. What is the minimum weight of the middle 95% of the players? A) 151
63.The starting salaries of individuals with an MBA degree are normally distributed with a mean of $40,000 and a standard deviation of $5,000. Refer to Exhibit 6-4. What is the random variable in this experiment? A) starting salaries
64. The starting salaries of individuals with an MBA degree are normally distributed with a mean of $40,000 and a standard deviation of $5,000. Refer to Exhibit 6-4. What is the probability that a randomly selected individual with an MBA degree will get a starting salary of at least $30,000? A) 0.9772
65.The starting salaries of individuals with an MBA degree are normally distributed with a mean of $40,000 and a standard deviation of $5,000. Refer to Exhibit 6-4. What is the probability that a randomly selected individual with an MBA degree will get a starting salary of at least $47,500? A) 0.0668
66.The starting salaries of individuals with an MBA degree are normally distributed with a mean of $40,000 and a standard deviation of $5,000. Refer to Exhibit 6-4. What percentage of MBAs will have starting salaries of $34,000 to $46,000? A) 76.98%
67.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 random variable in this experiment? A) Weight of items produced by a machine
68.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 will weigh more than 10 ounces? A) 0.1587
69.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 will weigh between 11 and 12 ounces? A) 0.0440
70.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) 3.22%
71.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 between 6.4 and 8.9 ounces? A) 0.4617
72.The life expectancy of a particular brand of tire is normally distributed with a mean of 40,000 and a standard deviation of 5,000 miles. Refer to Exhibit 6-6. What is the random variable in this experiment? A) Life expectancy of this brand of tire
73.The life expectancy of a particular brand of tire is normally distributed with a mean of 40,000 and a standard deviation of 5,000 miles. Refer to Exhibit 6-6. What is the probability that a randomly selected tire will have a life of at least 30,000 miles? A) 0.9772
74.The life expectancy of a particular brand of tire is normally distributed with a mean of 40,000 and a standard deviation of 5,000 miles. Refer to Exhibit 6-6. What is the probability that a randomly selected tire will have a life of at least 47,500 miles?
A) 0.0668
75.The life expectancy of a particular brand of tire is normally distributed with a mean of 40,000 and a standard deviation of 5,000 miles. Refer to Exhibit 6-6. What percentage of tires will have a life of 34,000 to 46,000 miles? A) 76.98%
76.The life expectancy of a particular brand of tire is normally distributed with a mean of 40,000 and a standard deviation of 5,000 miles. Refer to Exhibit 6-6. What is the probability that a randomly selected tire will have a life of exactly 47,500 miles? A) 0
77.f(x) = (1/10) e^-x/10 x ≥ 0. Refer to Exhibit 6-7. The mean of x is _____.
A) 10
78.(x) = (1/10) e^-x/10 x ≥ 0. Refer to Exhibit 6-7. The probability that x is less than 5 is _____.
A) 0.3935
79.f(x) = (1/10) e^-x/10 x ≥ 0. Refer to Exhibit 6-7. The probability that x is between 3 and 6 is _____.
A) 0.1920
80.Excel’s NORM.S.DIST function can be used to compute _____.
A) cumulative probabilities for a standard normal z value
81.Excel’s NORM.S.INV function can be used to compute _____.
A) the standard normal z value given a cumulative probability
82.Excel’s NORM.DIST function can be used to compute _____.
A) cumulative probabilities for a normally distributed x value
83.Excel’s NORM.INV function can be used to compute _____.
A) the normally distributed x value given a cumulative probability
84.When using Excel’s EXPON.DIST function, one should choose TRUE for the third input if _____ is desired.
A) a cumulative probability
85.About 95.4% of the values of a normal random variable are within approximately how many standard deviations of its mean?
A) ±2