Give an example representing a discrete probability distribution and another example representing a continuous probability distribution. Explain why your choices are discrete and continuous.
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If a variable can take on any value between two specified values, it is called a continuous variable; otherwise, it is called a discrete variable.
Some examples will clarify the difference between discrete and continuous variables.
Suppose the fire department mandates that all fire fighters must weigh between 150 and 250 pounds. The weight of a fire fighter would be an example of a continuous variable; since a fire fighter's weight could take on any value between 150 and 250 pounds.
Suppose we flip a coin and count the number of heads. The number of heads could be any integer value between 0 and plus infinity. However, it could not be any number between 0 and plus infinity. We could not, for example, get 2.5 heads. Therefore, the number of heads must be a discrete variable.
Just like variables, probability distributions can be classified as discrete ...
An example representing a discrete probability distribution and another example representing a continuous probability distribution is given. The choices of discrete and continuous are explained.