Unformatted text preview: E ( μ t  Y 1 ,... ,Y t ), for t = 1 , 2 ,... , 10. Attach your R code and the results. 2. Suppose we want to estimate μ = E ( X ). Let C be a random variable. Assume we know E ( C ) = μ . Then we can form X ( b ) = bX + (1 − b ) C such that E [ X ( b )] = E ( X ). If C is correlated with X and V ar ( X ) n = Cov ( X,C ), show that we can always choose a proper b so that X ( b ) has smaller variance than X ....
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 Spring '08
 Chen
 Standard Deviation, Variance, Probability theory, probability density function, Normal Mean Shift

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