Chapter 5: Discrete Probability Distributions

The variance is a measure of dispersion or variability in the random variable. It is a weighted average of the
1. square root of the deviations from the mean
2. square root of the deviations from the median
3. squared deviations from the median
4. square deviations from the mean
A numerical description of the outcome of an experiment is a random
1. description
2. outcome
3. number
4. variable
A random variable that can take on only a finite or countable number of values is known as a
1. continuous random variable
2. discrete random variable
3. discrete probability
4. none of the above
A random variable that may take on any value in an interval or collection of intervals is known as a
1. continuous random variable
2. discrete random variable
3. continuous probability function
4. none of the above
A weighted average of the value of a random variable where the probability function provides weights is known as
1. a probability function
2. a random variable
3. the expected value
4. none of the above
The following represents the probability distribution for the daily demand of microcomputers at a local store:

The expected daily demand is:
1. 1.0
2. 2.2
3. 2, since it has the highest probability
4. of course 4, since it is the largest demand level
5. 0
The probability distribution for a discrete random variable which is used to compute the probability of x successes in n trials is known as the
1. normal probability distribution
2. standard normal distribution
3. binomial probability distribution
4. none of the above
The probability distribution for a discrete random variable that is used to compute the probability of x occurrences of an event over a specified interval is known as
1. the normal probability distribution
2. the standard normal distribution
3. a discrete random variable
4. a linear function
5. the Poisson probability distribution
Which of the following is a required condition for a discrete probability function?
1. Σf(x)= 0
2. Σf(x)= 1
3. f(x) < 0
4. f(x) > 0
5. none of the above
The expected value of a binomial probability distribution is
1. E(x) = pn(1 - n)
2. E(x) = np(1 - p)
3. E(x) = np
4. E(x) = p(1 - n)