The number of accidents that occur annually on a bust stretch of highway is an example of:

-A Discrete Random Variable

-A Continuous Random Variable

-Expected Value of a Discrete Random Variable

-Expected Value of a Continuous Random Variable

A Discrete Random Variable

True, since the random variable is countable and represent the definition of a discrete random variable.

Step-by-step explanation:

Previous concepts

A discrete random variable has a "enumerable or countable number of possible values".

A continuous random variable is a random variable "where the data can take infinitely many values".

Based on this we can analyze one by one the options:

A Discrete Random Variable

True, since the random variable is countable and represent the definition of a discrete random variable.

A Continuous Random Variable

False, It's not possible since the number of accidents can't be expressed on decimals like for example 4.6, since by the nature of the random variable is discrete.

Expected Value of a Discrete Random Variable

False, it can't represent the expected value of a discrete random variable because in some cases the expected values for a discrete variable is not an integer or countable value, so this statement not applies for all the possible cases.

Expected Value of a Continuous Random Variable

False, since the random variable can't be a continuous variable, then it can't be possible that represent the expected value of a continuous distribution