# M 384G/374G/CAM384T CONDITIONAL AND MARGINAL MEANS AND VARIANCES. I. Question: How are conditional means E(Y X) and marginal means E(Y) related?

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1 1 M 3G/37G/CAM3T CONDITIONAL AND MARGINAL MEANS AND VARIANCES I. Question: How are conditional means E(Y X) and marginal means E(Y) related? Simple example: Population consisting of men, n women. Y = height X = sex Categorical, two values: Male, Female So there are two conditional means: E(Y male) = (Sum of all men s heights)/ E(Y female) = (Sum of all women s heights)/n Then Sum of all men s heights = E(Y male) Sum of all women's heights = n E(Y female) The marginal mean is E(Y) = (Sum of all heights)/( + n ) = (Sum of men' s heights) + (Sum of women's heights) + n = E(Y male) + n E(Y female) + n n = 1 n E(Y male) + E(Y female) + n + n = (proportion of males)( E(Y male) + (proportion of females)( E(Y female) = (probability of male)( E(Y male) + (probability of female)( E(Y female) Thus: The marginal mean is the weighted average of the conditional means, with weights equal to the probability of being in the subgroup determined by the corresponding value of the conditioning variable. Similar calculations show: If we have a population made up of m subpopulations pop 1, pop,, pop m (equivalently, if we are conditioning on a categorical variable with m values -- e.g., the age of a fish), then m " E(Y) = Pr(pop k )E(Y pop k ) k =1 e.g., for our fish, pop k = fish of age k, and

2 " E(length) = Pr(Age = k)e( Length Age = k) k =1 Rephrasing in terms of the categorical variable X defining the subpopulations, Stated in words: " E(Y) = Pr(x)E(Y X = x) all values x of X The analogue for conditioning on a continuous variable X is: E(Y) = # \$ f X (x)e ( Y x) dx, "# where f X (x) is the probability density function (pdf) of X. Note: 1. There are analogous results for conditioning on more than one variable.. The analogous result for sample means is y = II. A second (related) relationship between marginal and conditional means for populations: Consider E(Y X) as a new random variable U as follows: Randomly pick an x from the distribution of X. The new r.v. U has value E(Y X = x). Example: Y = height, X = sex Randomly pick a person from the population in question. " µ U = f = E(Y X = female) if the person is female # \$ µ m = E(Y X = male) if the person is male Question to think about: What might cause P(U = u) to be high? [Hint: There are two ways this might arise]

3 3 Consider the expected value of this new random variable. (e.g., the expected value of the mean height for the sex of a randomly selected person from the given population. In this case, we would expect E(U) to depend on the proportion of the population which is of each sex.) If U is discrete, then E(U) = " P(u)u [Why?] All possible values of U Example: For U = E(height sex), the values taken on by U are and, with respective probabilities and, so E(U) =, which from Part I is just. In other words, E(E(ht sex) = The same reasoning works in general, showing that: The expected value of the conditional means is the weighted average of the conditional means marginal mean, which from Part 1 is just the marginal mean i.e., E(E(Y X)) = weighted average of conditional means = E(Y) III. CONDITIONAL AND MARGINAL VARIANCE Marginal Variance: The definition of the (population) (marginal) variance of a random variable Y is Var(Y) = E([Y - E(Y)] ) What does this say in words (and pictures)? There is another formula for Var(Y) that is sometimes useful in computing variances or proving things about them. It can be obtained by multiplying out the squared expression in the definition: Var(Y) = E([Y - E(Y)] ) = E(Y - YE(Y) + [E(Y)] ) =

4 (Fill in details, and say the final result in words!) Conditional Variance: Similarly, if we are considering a conditional distribution Y X, we define the conditional variance Var(Y X) = Variance of the conditional distribution Y X = E([Y - E(Y X)] X) (Note that both expected values here are conditional expected values.) What does this say in words (and pictures)? Exercise: Derive another formula for the conditional variance, analogous to the second formula for the marginal variance. (And say it in words!) Conditional Variance as a Random Variable: As with E(Y X), we can consider Var(Y X) as a random variable. For example, if Y = height and X = sex for persons in a certain population, then Var(height sex) is the variable which assigns to each person in the population the variance of height for that person's sex. IV. CONNECTING MEANS AND VARIANCES Expected Value of the Conditional Variance: Since Var(Y X) is a random variable, we can talk about its expected value. Using the formula Var(Y X) = E(Y X) - [E(Y X)], we have E(Var(Y X)) = E(E(Y X)) - E([E(Y X)] ) We have already seen that the expected value of the conditional expectation of a random variable is the expected value of the original random variable, so applying this to Y gives (*) E(Var(Y X)) = E(Y ) - E([E(Y X)] ) Variance of the Conditional Expected Value: For what comes next, we will need to consider the variance of the conditional expected value. Using the second formula for variance, we have Var(E(Y X)) = E([E(Y X)] ) - [E(E(Y X))] Since E(E(Y X)) = E(Y), this gives (**)Var(E(Y X)) = E([E(Y X)] ) - [E(Y)].

5 Putting It Together: Note that (*) and (**) both contain the term E([E(Y X)] ), but with opposite signs. So adding them gives: E(Var(Y X)) + Var(E(Y X)) = E(Y ) - [E(Y)], which is just Var(Y). In other words, (***) Var(Y) = E(Var(Y X)) + Var(E(Y X)). In words: The marginal variance is the sum of the expected value of the conditional variance and the variance of the conditional means. Consequences: 1) This says that two things contribute to the marginal (overall) variance: the expected value of the conditional variance, and the variance of the conditional means. (See Exercise) Moreover, Var(Y) = E(Var(Y X)) if and only if Var(E(Y X)) =. What would this say about E(Y X)? ) Since variances are always non-negative, (***) implies Var(Y) E(Var(Y X)). 3) Since Var(Y X), E(Var(Y X)) must also be. (Why?). Thus (***) implies Var(Y) Var(E(Y X)). Moreover, Var(Y) = Var(E(Y X)) if and only if E(Var(Y X)) =. What would this imply about Var(Y X) and about the relationship between Y and X? ) Another perspective on (***) (cf. Textbook, pp. 3-37): Note that: i) E(Var(Y X) is a weighted average of Var(Y X) ii) Var(E(Y X) = E([E(Y X) - E(E(Y X))] ) = E([E(Y X) - (E(Y)] ), which is a weighted average of [E(Y X) - (E(Y)] Thus, (***) says that Var(Y) is a weighted mean of Var(Y X) plus a weighted mean of [E(Y X) - (E(Y)] (and is a weighted mean of Var(Y X) if and only if all conditional expected values E(Y X) are equal to the marginal expected value E(Y).)

6 EXERCISE: Which contributes most to Var(Y): Var(E(Y X)) or E(Var(Y X))? A x y 1 B x y 3 7 C. 1 1 x 1 - y 1 1

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