Let X be a random variable. Suppose we try to use a constant real number â to guess the value of X. Let E(X – â)² be the mean squared error (MSE) of this estimation. (i) By writing X − â = (X − EX) + (E X − î), show that - E(X − â)² = Var(X) + (E X − â)². (ii) Conclude that the MSE is minimized for î = EX and the minimum MSE equals to Var(X).

A First Course in Probability (10th Edition)
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ISBN:9780134753119
Author:Sheldon Ross
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Chapter1: Combinatorial Analysis
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Let X be a random variable. Suppose we try to use a constant real number â to guess
the value of X. Let E(X – î)² be the mean squared error (MSE) of this estimation.
(i) By writing X − â = (X − E X) + (EX − 2), show that
E(X)² = Var(X) + (EX - 2)².
=
(ii) Conclude that the MSE is minimized for î
Var(X).
EX and the minimum MSE equals to
Transcribed Image Text:Let X be a random variable. Suppose we try to use a constant real number â to guess the value of X. Let E(X – î)² be the mean squared error (MSE) of this estimation. (i) By writing X − â = (X − E X) + (EX − 2), show that E(X)² = Var(X) + (EX - 2)². = (ii) Conclude that the MSE is minimized for î Var(X). EX and the minimum MSE equals to
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