The probability distribution of the number of accidents in Grand Rapids, Michigan, each day is given by

x
0
1
2
3
4
5
P(x)
.20
.15
.25
.15
.20
.05
This distribution is an example of:

What are your choices?

Oops, I forgot.

Here it is,

an expected value distribution
b. a discrete probability distribution
c. none of these
d. a continuous probability distribution
e. a conditional probability distribution

This probability distribution is an example of a discrete probability distribution.

To determine the type of probability distribution, we need to analyze the given data.

In this case, we have a probability distribution that lists different values of the random variable "x" (which represents the number of accidents in Grand Rapids each day) along with their respective probabilities (P(x)).

The fact that each value of "x" has a probability assigned to it suggests that this is a discrete probability distribution.

Now, to specifically identify the type of distribution, we can look at the pattern of probabilities.

In this case, the probabilities are not equally distributed, and the values gradually increase and then decrease. This type of distribution is known as a skewed distribution, specifically a right-skewed distribution.

The given probability distribution, with a higher concentration of accidents at lower values and a long tail on the right side, is an example of a right-skewed discrete probability distribution.