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A probability distribution tells us the probability that a random variable takes on certain values.

For example, the following probability distribution tells us the probability that a certain soccer team scores a certain number of goals in a given game:

To find the **expected value** of a probability distribution, we can use the following formula:

Î¼ = Î£x * P(x)

where:

- x: Data value
- P(x): Probability of value

For example, the expected number of goals for the soccer team would be calculated as:

Î¼ = 0*0.18Â +Â 1*0.34Â +Â 2*0.35Â +Â 3*0.11Â +Â 4*0.02Â =Â Â **1.45** goals.

To calculate expected value of a probability distribution in R, we can use one of the following three methods:

#method 1 sum(vals*probs) #method 2 weighted.mean(vals, probs) #method 3 c(vals %*% probs)

All three methods will return the same result.

The following examples show how to use each of these methods in R.

**Example 1: Expected Value Using sum()**

The following code shows how to calculate the expected value of a probability distribution using the **sum()** function:

#define values vals #define probabilities probs #calculate expected value sum(vals*probs) [1] 1.45

**Example 2: ****Expected Value Using weighted.mean()**

The following code shows how to calculate the expected value of a probability distribution using the built-in **weighted.mean()** function in R:

#define values vals #define probabilities probs #calculate expected value weighted.mean(vals, probs) [1] 1.45

**Example 3: Expected Value Using c()**

The following code shows how to calculate the expected value of a probability distribution using the built-in **c()** function in R:

#define values vals #define probabilities probs #calculate expected value c(vals %*% probs) [1] 1.45

Notice that all three methods returned the same expected value.

**Additional Resources**

How to Calculate Mean in R

How to Calculate Geometric Mean in R

How to Calculate Weighted Mean in R