Let x be a continuous random variable. Prove var(x) . (assuming var(x) exist)
Let x be a continuous random variable. Prove var(x) . (assuming var(x) exist)
3. Let X be a continuous random variable with E(X)-μ and Var(X)-σ2 < oo. Suppose we try to estimate μ using these two estimators from a random sample X, , X,: For what a and b are both estimators unbiased and the relative efficiency of μι to is 45n?
Let X be a continuous random variable. Prove that: P(21-; < X < xạ) = 1 - a.
Let X be a continuous random variable with the following density function. Find E(X) and var(X). 6e -7x for x>0 f(x) = { for xso 6 E(X) = 49 var(X) =
Let at XW) =andom variable. Prove III. VARIANCE PROOFS (SINGLE RANDOM VARIABLE) Let S be a sample space. Let a, b, c be real numbers. a) Let X(W) = c for all w ES. Prove that Var(X) = 0. b) Let Y be a random variable. Prove that Var(Y + b) = Var(Y). c) Let Z be a random variable. Prove that Var(az) = a-Var(Z). d) Let W be a random variable. Use parts (b)-(c) to prove that Var(aW+b) =...
Let X be a continuous random variable whose PDF is Let X be a continuous random variable whose PDF is: f(x) = 3x^2 for 0 <x<1 Find P(X<0.4). Use 3 decimal points.
What is Var[3X]? Let X be a random variable such that Var[X] = 5 and E[X] = 4.
2. Let Xbe a random variable with a continuous uniform density between -1 and 1, i.e, X U(-1,1) Random variable Y is defined by the following transformation: (1) Var(Y)-? 2. Let Xbe a random variable with a continuous uniform density between -1 and 1, i.e, X U(-1,1) Random variable Y is defined by the following transformation: (1) Var(Y)-?
2. Let Xbe a random variable with a continuous uniform density between -1 and 1, i.e, X U(-1,1) Random variable Y is defined by the following transformation: (1) Var(Y)-? 2. Let Xbe a random variable with a continuous uniform density between -1 and 1, i.e, X U(-1,1) Random variable Y is defined by the following transformation: (1) Var(Y)-?
(a) If var[X o2 for each Xi (i = 1,... ,n), find the variance of X = ( Xi)/n. (b) Let the continuous random variable Y have the moment generating function My (t) i. Show that the moment generating function of Z = aY b is e*My(at) for non-zero constants a and b ii. Use the result to write down the moment generating function of W 1- 2X if X Gamma(a, B) (a) If var[X o2 for each Xi (i...
4. (20%) Let X be a continuous random variable with the following PDF Sce-4x 0<x fx(x) = to else where c is a positive constant. (a) (5%) Find c. (b) (5%) Find the CDF of X, Fx(x). (c) (5%) Find Prob{2<x<5} (d)(5%) Find E[X], and Var(X).