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Expected Value

Expected Value. Section 7.4 (partially). Section Summary. Expected Value Linearity of Expectations Independent Random Variables. Expected Value. Definition : The expected value (or expectation or mean ) of the random variable X ( s ) on the sample space S is equal to

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Expected Value

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  1. Expected Value Section 7.4 (partially)

  2. Section Summary • Expected Value • Linearity of Expectations • Independent Random Variables

  3. Expected Value Definition: The expected value (or expectation or mean) of the random variable X(s) on the sample space S is equal to Example-Expected Value of a Die: Let X be the number that comes up when a fair die is rolled. What is the expected value of X? Solution: The random variable X takes the values 1, 2, 3, 4, 5, or 6. Each has probability 1/6. It follows that

  4. Expected Value Theorem 1: If X is a random variable and p(X = r) is the probability that X = r, so that then see the text for the proof Example: what is the expected sum of the numbers that appear when two fair dice are rolled? Hint: compute p(X=k) for each k from 2 to 12 and use Theorem 1 to get expected sum 7.

  5. Expected Value Theorem 2: The expected number of successes when n mutually independent Bernoulli trials are performed is np, where p is the probability of success on each trial. see the text for the proof Example: what is the expected number of heads that come up when a fair coin is flipped 5 times. Solution: By Theorem 2 with p=1/2 and n=5, we see that the expected number of heads is 2.5 .

  6. Linearity of Expectations The following theorem tells us that expected values are linear. For example, the expected value of the sum of random variables is the sum of their expected values. Theorem 3: If Xi, i = 1, 2, …,n with n a positive integer, are random variables on S, and if a and b are real numbers, then (i) E(X1 + X2 + …. + Xn) = E(X1 )+ E(X2) + …. + E(Xn) (ii) E(aX + b) = aE(X) + b. see the text for the proof

  7. Independent Random Variables Definition 3: The random variables X and Y on a sample space S are independent if p(X = r1 and Y = r2) = p(X = r1)∙p(Y = r2). Theorem 5: If X and Y are independent variables on a sample space S, then E(XY) = E(X)E(Y). see text for the proof

  8. Independent Random Variables Theorem 5: If X and Y are independent variables on a sample space S, then E(XY) = E(X)E(Y). Example: Let X be the number that comes up on the first die when two fair dice are rolled and Y be the sum of the numbers appearing on the two dice. Show that E(X)E(Y) is not equal E(XY). Answer: E(X)=7/2. E(Y)= 7. E(XY)= 329/12. For example,

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