There seems to be a lot of confusion in relation to random variables. Part of this has to do with students being pressed onto new subjects without necessarily understanding the previous material adequately. Part of this has to do with the plethora of new functions that are introduced in relation to these random variables (e.g., a random variable X is itself a function in the probability space, it has a p.m.f., a m.g.f., and a c.d.f.). And part of this confusion has to do with a variety of ways to compute things: probabilities, expectations, and moments. Before you allow yourself to be engulfed by the onslaught of waves of probability theory crashing down on you, take a breath (of air) and consider the following.

Probabilities most fundamentally describe random experiments. The probability space (or sample space) describes the possible outcomes of the experiments in as much detail as necessary to completely characterize that experiment. A random variable is a single numeric summary of the nature of the experimental outcome. There are typically many different outcomes of the experiment that can lead to the same value for the random variable. Furthermore, a single experiment can provide the information for many different random variables, each of which summarizes a different aspect of the experiment.

For example, suppose our experiment consists of rolling two standard 6-sided dice, one of which is red and the other is green. There are 36 distinct outcomes. We might be interested in the value of the red die, which value we could assign to a random variable R. We might instead be interested in the value of the green die, which value we could assign to a random variable G. Other values of interest might be the largest of the two values, the smallest of the two values, the sum of the values, the difference between the dice, the greatest common factor between the values, etc. The list could go on forever, with each summary value corresponding to a different random variable.

The random variables R and G are independent because the roll of one does not influence the other. They are also identically distributed. The probability mass function (p.m.f.) for the random variable describes the probabilities of individual values for R. We either need a formula for an arbitrary value or a table showing all of the values to describe this function. For this random variable, we have R(x)=1/6 for each of the values in the support S={1,2,3,4,5,6}. The expected value is defined as a sum over all possible values in the support

For a single die roll (R or G), we have E[R] = 1/6(1)+1/6(2)+...+1/6(6) = 3.5.

Let us now consider another random variable, S, which represents the sum of the two values. That is, S=R+G. The support for S is the set {2,3,...,12}. We could compute the p.m.f. for this random variable: f(7)=6/36, f(6)=f(8)=5/36, f(5)=f(9)=4/36, f(4)=f(10)=3/36, f(3)=f(11)=2/36, and f(2)=f(12)=1/36. And we could compute the expected value of the random variable using the definition of mathematical expectation:

However, since S=R+G, we have a lovely little theorem that allows us to use a sum rule:

This is much easier than calculating using the definition.

So, we learn a lesson: if we can express a random variable as a sum of easier random variables, expected value may be more effectively calculated using these individual terms.

To continue, let us consider the distance between the two rolls. That is, we introduce a random variable X = |R-G| to represent the distance between the rolls. The smallest possible value for X is 0, which occurs when the dice are the same. The largest possible value for X is 5, which occurs when one die is 1 and the other is 6. So the support for X is the set S={0,1,2,3,4,5}. In this example, a table for the p.m.f. is much easier, computed based on the number of ways to obtain each distance:

f(0)=6/36, f(1)=10/36, f(2)=8/36, f(3)=6/36, f(4)=4/36, f(5)=2/36

For this problem, the random variable X is not easily expressed as a sum. So we must compute expected value using the original definition:

In summary, a random variable is a single number that summarizes some aspect of a random experiment. The p.m.f. of the random variable gives probabilities of individual outcomes. The expected value (or mathematical expectation) computes the average of a random quantity weighted by appropriate probabilities of those values.

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