Statistics Tutorial: Attributes of Random Variables

Just like variables from a data set, random variables are described by measures of central tendency (i.e., mean and median) and measures of variability (i.e., standard deviation and variance). This lesson shows how to compute these measures for discrete random variables.

Mean of a Discrete Random Variable

The mean of the discrete random variable X is also called the expected value of X. Notationally, the expected value of X is denoted by E(X). Use the following formula to compute the mean of a discrete random variable.

E(X) = μ_x = Σ [ x_i * P(x_i) ]

where x_i is the value of the random variable for outcome i, μ_x is the mean of random variable X, and P(x_i) is the probability that the random variable will be outcome i.

Example 1

In a recent little league softball game, each player went to bat 4 times. The number of hits made by each player is described by the following probability distribution.

What is the mean of the probability distribution?

(A) 1.00
(B) 1.75
(C) 2.00
(D) 2.25
(E) None of the above.

Solution

The correct answer is E. The mean of the probability distribution is 2.15, as defined by the following equation.

E(X) = Σ [ x_i * P(x_i) ]
E(X) = 0*0.10 + 1*0.20 + 2*0.30 + 3*0.25 + 4*0.15 = 2.15

Median of a Discrete Random Variable

The median of a discrete random variable is the "middle" value. It is the value of X for which P(X < x) is greater than or equal to 0.5 and P(X > x) is greater than or equal to 0.5.

Consider the problem presented above in Example 1. In Example 1, the median is 2; because P(X < 2) is equal to 0.60, and P(X > 2) is equal to 0.70. The computations are shown below.

P(X < 2) = P(x=0) + P(x=1) + P(x=2) = 0.10 + 0.20 + 0.30 = 0.60

P(X > 2) = P(x=2) + P(x=3) + P(x=4) = 0.30 + 0.25 + 0.15 = 0.70

Variability of a Discrete Random Variable

The standard deviation of a discrete random variable (σ) is equal to the square root of the variance of a discrete random variable (σ²). The equation for computing the variance of a discrete random variable is shown below.

σ² = Σ [ x_i - E(x) ]² * P(x_i)

where x_i is the value of the random variable for outcome i, P(x_i) is the probability that the random variable will be outcome i, E(x) is the expected value of the discrete random variable x.

Example 2

The number of adults living in homes on a randomly selected city block is described by the following probability distribution.

What is the standard deviation of the probability distribution?

(A) 0.50
(B) 0.62
(C) 0.79
(D) 0.89
(E) 2.10

Solution

The correct answer is D. The solution has three parts. First, find the expected value; then, find the variance; then, find the standard deviation. Computations are shown below, beginning with the expected value.

E(X) = Σ [ x_i * P(x_i) ]
E(X) = 1*0.25 + 2*0.50 + 3*0.15 + 4*0.10 = 2.10

Now that we know the expected value, we find the variance.

σ² = Σ [ x_i - E(x) ]² * P(x_i)
σ² = (1 - 2.1)² * 0.25 + (2 - 2.1)² * 0.50 + (3 - 2.1)² * 0.15 + (4 - 2.1)² * 0.10
σ² = (1.21 * 0.25) + (0.01 * 0.50) + (0.81) * 0.15) + (3.61 * 0.10) = 0.3025 + 0.0050 + 0.1215 + 0.3610 = 0.79

And finally, the standard deviation is equal to the square root of the variance; so the standard deviation is sqrt(0.79) or 0.889.