Isye 2027

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Unformatted text preview: j ) = ij. Each 26 CHAPTER 2. DISCRETE-TYPE RANDOM VARIABLES 4/36 p (k) Y 3/36 2/36 1/36 k 5 10 20 15 25 30 35 Figure 2.3: The pmf for the product of two fair dice of the 36 possible values of (X1 , X2 ) has probability 1/36, so we have E [Y ] = = = 1 36 6 6 ij i=1 j =1 1 36 6 i i=1 (21)2 36 6 = j j =1 7 2 2 = 49 = 12.25. 4 Variance and standard deviation Suppose you are to be given a payment, with the size of the payment, in some unit of money, given by either X or by Y, described as follows. The random 1 999 variable X is equal to 100 with probability one, whereas pY (100000) = 1000 and pY (0) = 1000 . Would you be equally happy with either payment? Both X and Y have mean 100. This example illustrates that two random variables with quite different pmfs can have the same mean. The pmf for X is concentrated on the mean value, while the pmf for Y is considerably spread out. The variance of a random variable X is a measure of how spread out the pmf of X is. Letting µX = E [X ], the variance is defined by: Var(X ) = E [(X − µX )2...
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This note was uploaded on 02/09/2014 for the course ISYE 2027 taught by Professor Zahrn during the Spring '08 term at Georgia Institute of Technology.

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