34 mse for l x 1 14 2 1 2 2 2 1 2 2 b

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: 159 (a) For any n Var(Sn ) = Cov(X1 + · · · + Xn , X1 + · · · + Xn ) n n = Cov(Xi , Xj ) i=1 j =1 n−1 = n Cov(Xi , Xi+1 ) + i=1 n Cov(Xi , Xi ) + i=1 Cov(Xi , Xi−1 ) i=2 ≤ (n − 1)(0.1) + n + (n − 1)(0.1) < (1.2)n. (b) Therefore, Var( S100 ) = 100 inequality, P 4.9.2 S100 100 −5 ≥ 1 Var(S100 ) ≤ 0.012. (100)2 0 0.5 ≤ (0..012 = 0.048. 5)2 Also, E S100 100 = µ = 5. Thus, by Chebychev’s Central limit theorem The following version of the central limit theorem (CLT) generalizes the DeMoivre-Laplace limit theorem, discussed in Section 3.6.3. While the DeMoivre-Laplace limit theorem pertains to sums of independent, identically distributed Bernoulli random variables, the version here pertains to sums of independent, identically distributed random variables with any distribution, so long as their mean and variance are finite. Theorem 4.9.4 (Central limit theorem) Suppose X1 , X2 , . . . are independent, identically distributed random variables, each with mean µ and variance σ 2 . Let Sn = X1 + · · · + Xn . T...
View Full Document

This note was uploaded on 02/09/2014 for the course ISYE 2027 taught by Professor Zahrn during the Spring '08 term at Georgia Tech.

Ask a homework question - tutors are online