Chapter Terminology
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Chapter Four Terms

Chapter Four Terminology
Discrete Random Variable - A random variable that has either a finite number of values or a countable number of values. There may be an infinite number of them, but they result from a 'counting' process. For example, the number of students in this class is a discrete random variable.

Continuous Random Variable - Has infinitely many values, and those values can be associated with measurements on a continuous scale in such a way that there are no gaps or interruptions. Time and weight both good examples of continuous random variables.

Probability Distribution - A graph, table or formula that gives the probability for each random variable. It is a model that describes what will 'probably' happen if you conduct a procedure.

Expected Value - A discrete random variable denoted by E, such that it represents the average value of all possible outcomes. See the example in the textbook on page 189.

Binomial Probability Distribution - A discrete probability distribution with the following four requirements:
1. There are a fixed number of trials.
2. The trials are independent.
3. Each trial must the outcomes classified into TWO categories.
4. The probabilities must remain constant for every trial.