Let X=2N={x=(x1,x2,…):xi∈{0,1}} and define
d(x,y)=2∑(i≥1)(3^−i)*|xi−yi|.
Define f:X→[0,1] by f(x)=d(0,x), where 0=(0,0,0,…).
Prove that maps X onto...
Let X=2N={x=(x1,x2,…):xi∈{0,1}} and define
d(x,y)=2∑(i≥1)(3^−i)*|xi−yi|.
Define f:X→[0,1] by f(x)=d(0,x), where 0=(0,0,0,…).
Prove that maps X onto the Cantor set and satisfies
(1/3)*d(x,y)≤|f(x)−f(y)|≤d(x,y) for x,y∈2N.
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]...
Let x1, x2, ..., xk be linearly independent vectors in R n and
let A be...
Let x1, x2, ..., xk be linearly independent vectors in R n and
let A be a nonsingular n × n matrix. Define yi = Axi for i = 1, 2,
..., k. Show that y1, y2, ..., yk are linearly independent.
Consider the model Yi = 2 + βxi +E i , i = 1, 2, ....
Consider the model Yi = 2 + βxi +E i , i = 1, 2, . . . , n where
E1, . . . , E n be a sequence of i.i.d. observations from N(0, σ2
). and x1, . . . , xn are given constants.
(i) Find MLE for β and call it β. ˆ .
(ii) For a sample of size n = 7 let (x1, . . . , x7) = (0, 0, 1,
2,...
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 xi = i for i = 1, 2, . . . , 15, and let...
Let xi = i for i = 1, 2, . . . , 15, and let the corresponding
yi (i = 1, 2, . . . , 15) numbers be (in the order of indeces) 5,
15, 42, 57, 65, 68, 69, 83, 87, 98, 105, 108, 108, 108, 110.
Calculate the least squares regression line for these data.
Let Xi, i=1,...,n be independent exponential r.v. with mean
1/ui. Define Yn=min(X1,...,Xn), Zn=max(X1,...,Xn).
1. Define the...
Let Xi, i=1,...,n be independent exponential r.v. with mean
1/ui. Define Yn=min(X1,...,Xn), Zn=max(X1,...,Xn).
1. Define the CDF of Yn,Zn
2. What is E(Zn)
3. Show that the probability that Xi is the smallest one among
X1,...,Xn is equal to ui/(u1+...+un)
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,...
Let xi = i for i = 1, 2, . . . , 17, and let...
Let xi = i for i = 1, 2, . . . , 17, and let the corresponding
yi (i = 1, 2, . . . , 17)
numbers be (in the order of indeces) 5, 15, 42, 57, 65, 68, 69,
83, 87, 98, 105, 108,
108, 108, 110, 112, 116. Calculate the least squares regression
line for these data.
Find 95% CI for the quantities α, β, and σ^2 in the previous
problem.
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.