Transcription of Properties of Expected values and Variance
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Properties of Expected values and VarianceChristopher CrokeUniversity of PennsylvaniaMath 115 UPenn, Fall 2011 Christopher CrokeCalculus 115 Expected valueConsider a random variableY=r(X) for some functionr, + 3 so in this caser(x) =x2+ turns out (and wehave already used) thatE(r(X)) = r(x)f(x) is not obvious since by definitionE(r(X)) = xfY(x)dxwherefY(x) is the probability density function ofY=r(X).You get from one integral to the other by careful uses consequence isE(aX+b) = (ax+b)f(x)dx=aE(X) +b.
One consequence is E(aX + b) = Z 1 1 (ax + b)f(x)dx = aE(X) + b: (It is not usually the case that E(r(X)) = r(E(X)).) Similar facts old for discrete random variables. Christopher Croke Calculus 115. Expected value Consider a random variable Y = r(X) for some function r, e.g.
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