The probability histograms and density curves that picture the probability distributions of random variables resemble our earlier pictures of distributions of data. In describing data, we moved from graphs to numerical measures such as means and standard deviations. Now we will make the same move to expand our descriptions of the distributions of random variables. We can speak of the mean winnings in a game of chance or the standard deviation of the randomly varying number of calls a travel agency receives in an hour. In this section, we will learn more about how to compute these descriptive measures and about the laws they obey.

The mean of a random variable

In Chapter 1 (page 28), we learned that the mean is the average of the observations in a sample. Recall that a random variable X is a numerical outcome of a random process. Think about repeating the random process many times and recording the resulting values of the random variable. You can think of the value of a random variable as the average of a very large sample where the relative frequencies of the values are the same as their probabilities.

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If we think of the random process as corresponding to the population, then the mean of the random variable is a characteristic of this population. Here is an example.

If you play Tri-State Pick 3 several times, we would—as usual—call the mean of the actual amounts you win . The mean in Example 4.27 is a different quantity—it is the long-run average winnings you expect if you play a very large number of times.

Just as probabilities are an idealized description of long-run proportions, the mean of a probability distribution describes the long-run average outcome. We can’t call this mean , so we need a different symbol. The common symbol for the mean of a probability distributionmean μ is μ, the Greek letter mu. We used μ in Chapter 1 for the mean of a Normal distribution, so this is not a new notation. We will often be interested in several random variables, each having a different probability distribution with a different mean.

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To remind ourselves that we are talking about the mean of X, we often write μ_X rather than simply μ. In Example 4.27, μ_X = $0.50. Notice that, as often happens, the mean is not a possible value of X. You will often find the mean of a random variable X called the expected valueexpected value of X. This term can be misleading because we don’t necessarily expect one observation on X to be close to its expected value.

The mean of any discrete random variable is found just as in Example 4.27. It is an average of the possible outcomes, but a weighted average in which each outcome is weighted by its probability. Because the probabilities add to 1, we have total weight 1 to distribute among the outcomes. An outcome that occurs half the time has probability one-half and gets one-half the weight in calculating the mean. Here is the general definition.

Suppose that the random digits in Example 4.28 had a different probability distribution. In Example 4.12 (page 226), we described Benford’s law as a probability distribution that describes first digits of numbers in many real situations. Let’s calculate the mean for Benford’s law.

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Figure 4.13 locates the means of X and V on the two probability histograms. Because the discrete uniform distribution of Figure 4.13(a) is symmetric, the mean lies at the center of symmetry. We can’t locate the mean of the right-skewed distribution of Figure 4.13(b) by eye—calculation is needed.

What about continuous random variables? The probability distribution of a continuous random variable X is described by a density curve. Chapter 1 (page 54) showed how to find the mean of the distribution: it is the point at which the area under the density curve would balance if it were made out of solid material. The mean lies at the center of symmetric density curves such as the Normal curves. Exact calculation of the mean of a distribution with a skewed density curve requires advanced mathematics.¹³ The idea that the mean is the balance point of the distribution applies to discrete random variables as well, but in the discrete case, we have a formula that gives us this point.

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Statistical estimation and the law of large numbers

We would like to estimate the mean height μ of the population of all American women between the ages of 18 and 24 years. This μ is the mean μ_X of the random variable X obtained by choosing a young woman at random and measuring her height. To estimate μ, we choose an SRS of young women and use the sample mean to estimate the unknown population mean μ. In the language of Section 5.1 (page 282), μ is a parameter and is a statistic.

Statistics obtained from probability samples are random variables because their values vary in repeated sampling. The sampling distributions of statistics are just the probability distributions of these random variables.

It seems reasonable to use to estimate μ. An SRS should fairly represent the population, so the mean of the sample should be somewhere near the mean μ of the population. Of course, we don’t expect to be exactly equal to μ, and we realize that if we choose another SRS, the luck of the draw will probably produce a different .

If is rarely exactly right and varies from sample to sample, why is it nonetheless a reasonable estimate of the population mean μ? If we keep on adding observations to our random sample, the statistic is guaranteed to get as close as we wish to the parameter μ and then stay that close. We have the comfort of knowing that if we can afford to keep on measuring more women, eventually we will estimate the mean height of all young women very accurately. This remarkable fact is called the law of large numbers. It is remarkable because it holds for any population, not just for some special class such as Normal distributions.

The behavior of is similar to the idea of probability. In the long run, the proportion of outcomes taking any value gets close to the probability of that value, and the average outcome gets close to the distribution mean. Figure 4.1 (page 216) shows how proportions approach probability in one example. Here is an example of how sample means approach the distribution mean.

The mean μ of a random variable is the average value of the variable in two senses. By its definition, μ is the average of the possible values, weighted by their probability of occurring. The law of large numbers says that μ is also the long-run average of many independent observations on the variable. The law of large numbers can be proved mathematically starting from the basic laws of probability.

Thinking about the law of large numbers

The law of large numbers says broadly that the average results of many independent observations are stable and predictable. The gamblers in a casino may win or lose, but the casino will win in the long run because the law of large numbers says what the average outcome of many thousands of bets will be. An insurance company deciding how much to charge for life insurance and a fast-food restaurant deciding how many beef patties to prepare also rely on the fact that averaging over many individuals produces a stable result. It is worth the effort to think a bit more closely about so important a fact.

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The “law of small numbers” Both the rules of probability and the law of large numbers describe the regular behavior of chance phenomena in the long run. Psychologists have discovered that our intuitive understanding of randomness is quite different from the true laws of chance.¹⁴ For example, most people believe in an incorrect “law of small numbers.” That is, we expect even short sequences of random events to show the kind of average behavior that in fact appears only in the long run.

Some teachers of statistics begin a course by asking students to toss a coin 50 times and bring the sequence of heads and tails to the next class. The teacher then announces which students just wrote down a random-looking sequence rather than actually tossing a coin. The faked tosses don’t have enough “runs” of consecutive heads or consecutive tails. Runs of the same outcome don’t look random to us but are, in fact, common. For example, the probability of a run of three or more consecutive heads or tails in just 10 tosses is greater than 0.8.¹⁵ The runs of consecutive heads or consecutive tails that appear in real coin tossing (and that are predicted by the mathematics of probability) seem surprising to us. Because we don’t expect to see long runs, we may conclude that the coin tosses are not independent or that some influence is disturbing the random behavior of the coin.

Our intuition doesn’t do a good job of distinguishing random behavior from systematic influences. This is also true when we look at data. We need statistical inference to supplement exploratory analysis of data because probability calculations can help verify that what we see in the data is more than a random pattern.

How large is a large number? The law of large numbers says that the actual mean outcome of many trials gets close to the distribution mean μ as more trials are made. It doesn’t say how many trials are needed to guarantee a mean outcome close to μ. That depends on the variability of the random outcomes. The more variable the outcomes, the more trials are needed to ensure that the mean outcome is close to the distribution mean μ. Casinos understand this: the outcomes of games of chance are variable enough to hold the interest of gamblers. Only the casino plays often enough to rely on the law of large numbers. Gamblers get entertainment; the casino has a business.

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Is there a winning system for gambling? Serious gamblers often follow a system of betting in which the amount bet on each play depends on the outcome of previous plays. You might, for example, double your bet on each spin of the roulette wheel until you win—or, of course, until your fortune is exhausted. Such a system tries to take advantage of the fact that you have a memory even though the roulette wheel does not. Can you beat the odds with a system based on the outcomes of past plays? No. Mathematicians have established a stronger version of the law of large numbers that says that, if you do not have an infinite fortune to gamble with, your long-run average winnings μ remain the same as long as successive trials of the game (such as spins of the roulette wheel) are independent.

What if observations are not independent? You are in charge of a process that manufactures video screens for computer monitors. Your equipment measures the tension on the metal mesh that lies behind each screen and is critical to its image quality. You want to estimate the mean tension μ for the process by the average of the measurements. Alas, the tension measurements are not independent. If the tension on one screen is a bit too high, the tension on the next is more likely to also be high. Many real-world processes are like this—the process stays stable in the long run, but two observations made close together are likely to both be above or both be below the long-run mean. Again the mathematicians come to the rescue: as long as the dependence dies out fast enough as we take measurements farther and farther apart in time, the law of large numbers still holds.

Rules for means

You are studying flaws in the painted finish of refrigerators made by your firm. Dimples and paint sags are two kinds of surface flaw. Not all refrigerators have the same number of dimples: many have none, some have one, some two, and so on. You ask for the average number of imperfections on a refrigerator. The inspectors report finding an average of 0.7 dimple and 1.4 sags per refrigerator. How many total imperfections of both kinds (on the average) are there on a refrigerator? That’s easy: if the average number of dimples is 0.7 and the average number of sags is 1.4, then counting both gives an average of 0.7 + 1.4 = 2.1 flaws.

In more formal language, the number of dimples on a refrigerator is a random variable X that varies as we inspect one refrigerator after another. We know only that the mean number of dimples is μ_X = 0.7. The number of paint sags is a second random variable Y having mean μ_X = 1.4. (As usual, the subscripts keep straight which variable we are talking about.) The total number of both dimples and sags is another random variable, the sum X + Y. Its mean μ_X+Y is the average number of dimples and sags together. It is just the sum of the individual means μ_X and μ_Y. That’s an important rule for how means of random variables behave.

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Here’s another rule. The crickets living in a field have mean length of 1.2 inches. What is the mean in centimeters? There are 2.54 centimeters in an inch, so the length of a cricket in centimeters is 2.54 times its length in inches. If we multiply every observation by 2.54, we also multiply their average by 2.54. The mean in centimeters must be 2.54 × 1.2, or about 3.05 centimeters. More formally, the length in inches of a cricket chosen at random from the field is a random variable X with mean μ_X. The length in centimeters is 2.54X, and this new random variable has mean 2.54μ_X.

The point of these examples is that means behave like averages. Here are the rules we need.

Note that a + bX is a linear transformation of the random variable X.

The variance of a random variable

The mean is a measure of the center of a distribution. A basic numerical description requires, in addition, a measure of the spread or variability of the distribution. The variance and the standard deviation are the measures of spread that accompany the choice of the mean to measure center. Just as for the mean, we need a distinct symbol to distinguish the variance of a random variable from the variance s² of a data set. We write the variance of a random variable X as . Once again, the subscript reminds us which variable we have in mind. The definition of the variance of a random variable is similar to the definition of the variance s² given in Chapter 1 (page 38). That is, the variance is an average value of the squared deviation (X − μ_X)² of the variable X from its mean μ_X. As for the mean, the average we use is a weighted average in which each outcome is weighted by its probability in order to take account of outcomes that are not equally likely. Calculating this weighted average is straightforward for discrete random variables but requires advanced mathematics in the continuous case. Here is the definition.

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Rules for variances and standard deviations

What are the facts for variances that parallel Rules 1, 2, and 3 for means? The mean of a sum of random variables is always the sum of their means, but this addition rule is true for variances only in special situations. To understand why, take X to be the percent of a family’s after-tax income that is spent, and take Y to be the percent that is saved. When X increases, Y decreases by the same amount. Though X and Y may vary widely from year to year, their sum X + Y is always 100% and does not vary at all. It is the association between the variables X and Y that prevents their variances from adding.

If random variables are independent, this kind of association between their values is ruled out and their variances do add. Two random variables X and Y are independentindependence if knowing that any event involving X alone did or did not occur tells us nothing about the occurrence of any event involving Y alone.

Probability models often assume independence when the random variables describe outcomes that appear unrelated to each other. You should ask in each instance whether the assumption of independence seems reasonable.

When random variables are not independent, the variance of their sum depends on the correlationcorrelation between them as well as on their individual variances. In Chapter 2, we met the correlation r between two observed variables measured on the same individuals. We defined (page 101) the correlation r as an average of the products of the standardized x and y observations. The correlation between two random variables is defined in the same way, once again using a weighted average with probabilities as weights. We won’t give the details—it is enough to know that the correlation between two random variables has the same basic properties as the correlation r calculated from data. We use ρ, the Greek letter rho, for the correlation between two random variables. The correlation ρ is a number between −1 and 1 that measures the direction and strength of the linear relationship between two variables. The correlation between two independent random variables is zero.

Returning to family finances, if X is the percent of a family’s after-tax income that is spent and Y is the percent that is saved, then Y = 100 − X. This is a perfect linear relationship with a negative slope, so the correlation between X and Y is ρ = −1. With the correlation at hand, we can state the rules for manipulating variances.

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Because a variance is the average of squared deviations from the mean, multiplying X by a constant b multiplies by the square of the constant. Adding a constant a to a random variable changes its mean but does not change its variability. The variance of X + a is, therefore, the same as the variance of X. Because the square of −1 is 1, the addition rule says that the variance of a difference between independent random variables is the sum of the variances. For independent random variables, the difference X − Y is more variable than either X or Y alone because variations in both X and Y contribute to variation in their difference.

As with data, we prefer the standard deviation to the variance as a measure of the variability of a random variable. Rule 2 for variances implies that standard deviations of independent random variables do not add. To combine standard deviations, use the rules for variances. For example, the standard deviations of 2X and −2X are both equal to 2σ_X because this is the square root of the variance .

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If you buy a Pick 3 ticket, your winnings are W = X − 1 because the dollar you paid for the ticket must be subtracted from the payoff. Let’s find the mean and variance for this random variable.

Suppose now that you buy a $1 ticket on each of two different days. The payoffs X and Y on the two tickets are independent because separate drawings are held each day. Your total payoff is X + Y. Let’s find the mean and standard deviation for this payoff.

When we add random variables that are correlated, we need to use the correlation for the calculation of the variance but not for the calculation of the mean. Here is an example.

The negative correlation in Example 4.38 is due to the fact that, in this household, natural gas is used for heating and electricity is used for air-conditioning. So, when it is warm, the electricity charges are high and the natural-gas charges are low. When it is cool, the reverse is true. This causes the standard deviation of the sum to be less than it would be if the two bills were uncorrelated (see Exercise 4.79, page 263).

There are situations where we need to combine several of our rules to find means and standard deviations. Here is an example.

The correlation in this example illustrates an unfortunate fact about compliance and having an adequate diet. Some of the subjects in this study have diets that provide an adequate amount of calcium while others do not. The positive correlation between compliance and dietary intake tells us that those who have relatively high dietary intakes are more likely to take the assigned supplements. On the other hand, those subjects with relatively low dietary intakes, the ones who need the supplement the most, are less likely to take the assigned supplements.