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.