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When X and Y have expected values µX = µY = 0, Theorem 9.4 says that L (Y) = Show that this result is a special case of Theorem 9.7(a) when random vector Y is the 1- dimensional random variable Y.
Theorem 9.4
Random variables X and Y have expected values µX and µY, standard deviations σX and σY, and correlation coefficient ρ X,Y, The optimal linear mean square error (LMSE) estimator of X given Y is L (Y ) = a∗Y + b∗ and it has the following properties
Theorem 9.7(a)
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The problem related to Basic Statistics and it discuss about the significance of performing basic descriptive statistics prior to conducting inferential statistical tests
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A researcher is evaluating the influence of a treatment using a sample selected from a normally distributed population with a mean of µ = 80 and a standard deviation of σ = 20. The researcher expects a 12 point treatment effect and plans to use a ..
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