Chapter 6
1. Probability Distribution: For a random variable, a description or listing of all possible
values it can assume, along with their respective probabilities of occurrence.
2. Random Variable: A variable that can take on different values depending on the
outcome of an experiment.
3. Discrete Random Variable: A random variable that can take on only certain values
along an interval, with these values having gaps between them.
4. Continuous Random Variable: A random variable that can take a value at any point
along an interval. There are no gaps between the possible values.
5. Discrete Probability Distribution: The probability distribution for a discrete random
variable.
6. Mean of a Probability distribution: Expressed as  or E(x), this is the expected value
of the random variable, x. It is weighted average in which the possible values of x are
weighted according to their probabilities of occurrence.
7. Binomial Distribution: A distribution in which two values occur with the greatest
frequency.
8. Bernoulli Process: A process in which there are two or more consecutive tri-outcomes
(Success or failure), and in which the probability of a success remains the same from one
trial to the next; i.e., the trials are statistically independent from each other.
9. Sampling with Replacement: Sampling in which the item selected from the population
is observed or measured, then put back into the population.
10. Sampling without Replacement: Sampling in which the item selected from the
population is not returned to the population, after being observed or measured.
11. Poisson distribution: A discrete probability distribution applied to events for which the
probability of occurrence over a given span of time, space, or distance is extremely small.
12. Normal Distribution: A family of distributions, each member of which is bellshaped
and symmetrical, and has the mean, median, and mode allocated at the midpoint.