Question

a) Let Xi for i = 1,2,...n be random variables with E[Xi] = μi (not necessarily...

a) Let Xi for i = 1,2,...n be random variables with E[Xi] = μi (not necessarily independent). Show that E[∑ni =1 Xi] = [∑ni =1 μi]. Show from Definition

b) Suppose that random variables Yi for i = 1, 2,...,n are independent and identically distributed withE[Yi] =γ(gamma) and Var[Yi] = σ2, Use part (a) to show that E[Ybar] =γ(gamma).

(c) Suppose that random variables Yi for i = 1, 2,...,n are independent and identically distributed with E[Yi] =γ(gamma) and Var[Yi] = σ2. Show that Var[Ybar] =σ2/n. (recall E[(∑ni=1Yi)2] = Var[∑ni=1Yi] + E[∑ni=1Yi]2

Homework Answers

Answer #1

for query in above, comment.

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Let x1, x2 x3 ....be a sequence of independent and identically distributed random variables, each having...
Let x1, x2 x3 ....be a sequence of independent and identically distributed random variables, each having finite mean E[xi] and variance Var(xi). a)calculate the var (x1+x2) b)calculate the var(E[xi]) c) if n-> infinite, what is Var(E[xi])?
Suppose that X1,X2 and X3 are independent random variables with common mean E(Xi) = μ and...
Suppose that X1,X2 and X3 are independent random variables with common mean E(Xi) = μ and variance Var(Xi) = σ2. Let V= X2−X3 and W = X1− 2X2 + X3. (a) Find E(V) and E(W). (b) Find Var(V) and Var(W). (c) Find Cov(V,W). (d) Find the correlation coefficient ρ(V,W). Are V and W independent?
X1, … Xn are i.i.d. random variables, and E(Xi ) = 3β, Var(Xi ) = 3β^2...
X1, … Xn are i.i.d. random variables, and E(Xi ) = 3β, Var(Xi ) = 3β^2 , i = 1 … n, β > 0. Two estimators of β are defined as β̂ 1 = (X̅ /3) β̂ 2 = (n /3n+1 ) X̅ Show that MSE(β̂ 2) < MSE(β̂ 1) for a sample size of n = 3.
5. Let X1, X2, . . . be independent random variables all with mean E(Xi) =...
5. Let X1, X2, . . . be independent random variables all with mean E(Xi) = 7 and variance Var(Xi) = 9. Set Yn = X1 + X2 + · · · + Xn n (n = 1, 2, 3, . . .) (a) Find E(Y2) and E(Y5). (b) Find Cov(Y2, Y5). (c) Find E (Y2 | X1). (d) How should your answers from parts (a)–(c) be modified if the numbers “2”, “5”, “7” and “9” are replaced by m,...
question 01) Let Xi , i = 1 , 2 , 3 , … , 10...
question 01) Let Xi , i = 1 , 2 , 3 , … , 10 be normally distributed independent random variables X i ∼ N ( μ , σ2 ). Determine the mean μ = ...................... and the standard deviation σ = ..................... (round to the third decimal place), if it is known that 1/10 ∑10i=1 Xi ∼ N ( 1.5 , 0.3 ). need  μ and σ
Let X and Y be independent and identically distributed random variables with mean μ and variance...
Let X and Y be independent and identically distributed random variables with mean μ and variance σ2. Find the following: a) E[(X + 2)2] b) Var(3X + 4) c) E[(X - Y)2] d) Cov{(X + Y), (X - Y)}
Suppose that X1, X2, . . . , Xn are independent identically distributed random variables with...
Suppose that X1, X2, . . . , Xn are independent identically distributed random variables with variance σ2. Let Y1 = X2 +X3 , Y2 = X1 +X3 and Y3 = X1 + X2. Find the following : (in terms of σ2) (a) Var(Y1) (b) cov(Y1 , Y2 ) (c) cov(X1 , Y1 ) (d) Var[(Y1 + Y2 + Y3)/2]
X1,...,X81 ⇠ N(0,1) and Y1,...,Y81 ⇠ N(3,2 ). For each i, Corr(Xi,Yi)= 1/2. Let Zi =...
X1,...,X81 ⇠ N(0,1) and Y1,...,Y81 ⇠ N(3,2 ). For each i, Corr(Xi,Yi)= 1/2. Let Zi = Xi + Yi. 1. Compute Var(Zi). 2. Approximate P [ Zi > 243]. (Explain your answer.)
Let Xi, i = 1, 2..., 48, be independent random variables that are uniformly distributed on...
Let Xi, i = 1, 2..., 48, be independent random variables that are uniformly distributed on the interval [-0.5, 0.5]. (a) Find the probability Pr(|X1|) < 0.05 (b) Find the approximate probability P (|Xbar| ≤ 0.05). (c) Determine an approximation of a such that P(Xbar ≤ a) = 0.15
Let X i ~ Unif(0, 1) for 1 <= i <= n be IID (independent identically...
Let X i ~ Unif(0, 1) for 1 <= i <= n be IID (independent identically distributed) random variables. Let Y = max(X 1 , …, X n ). What is E(Y)?