Use integration to derive the moment-generating function MX (t) where fX (x) = (1/3) e^(−x/3)
for x > 0. (Since we are maily interested in t near 0, assume that t < 1/3 .) Then use MX (t)
to compute E(X), E(X^2), V (X), and E(X^3).
Use integration to derive the moment-generating function MX (t) where fX (x) = (1/3) e^(−x/3) for x > 0. (Since we ar...
The moment generating function (MGF) for a random variable X is: Mx (t) = E[e'X]. Onc useful property of moment generating functions is that they make it relatively casy to compute weighted sums of independent random variables: Z=aX+BY M26) - Mx(at)My (Bt). (A) Derive the MGF for a Poisson random variable X with parameter 1. (B) Let X be a Poisson random variable with parameter 1, as above, and let y be a Poisson random variable with parameter y. X...
12. let Mx(1) be the moment generating function of X. Show that (a) Mex+o(t) = eMx(at). (b) TX - Normal(), o?) and moment generating function of X is Mx (0) - to'p. Show that the random variable 2 - Normal(0,1) 13. IX. X X . are mutually independent normal random variables with means t o ... and variances o, o,...,0, then prove that X NOEL ?). 14. If Mx(1) be the moment generating function of X. Show that (a) log(Mx...
7. Derive the moment-generating function M(t) for X 1(a, X). 8. Expand the moment-generating function M(t) = ex+oft®/2 in a power series in t to compute E[X3] if X ~ N(1, 2).
Let X be a continuous random variable with values in [ 0, 1], uniform density function fX(x) ≡ 1 and moment generating function g(t) = (e t − 1)/t. Find in terms of g(t) the moment generating function for (a) −X. (b) 1 + X. (c) 3X. (d) aX + b.
Given f(x) = ( c(x + 1) if 1 < x < 3 0 else as a probability function for a continuous random variable; find a. c. b. The moment generating function MX(t). c. Use MX(t) to find the variance and the standard deviation of X.
Use the given moment-generating function, Mx(t), to identify the distribution of the random variable, X in each of the following cases. (Specify the exact type of distribution and the value(s) of any relevant parameters(s): 1. (a) M(-3 (b) M() exp(2e -2) Ce) M T112t)3 (f) Mx(t) = ( 1-3t 10 ) (d) Mx(t)= exp(2t2_t) (e) Mx(t)= - m01 -2t)!
(3 marks) The moment generating function of a random variable X is given by MX(t) = 24 20 < - In 0.6. Find the mean and standard deviation of X using its moment generating function.
(9 points) In this question, we will find E(X), the nth moment of X,, where X, N(0,0%) is normally distributed with mean 0 and variance oz. (a) (3 points) A function po() is a probability measure on R if po(t) > 0 V2 € R and Po(x) dx = 1. By showing Lee* dar = Vī, show that po(x) = 2 e is a probability measure. (b) (3 points) Completing the square, compute the moment generating function of X, V20...
1. Find Fx in terms of φ (t). Is X a continuous random variable ? 2. Compute p(X 0) 3. Compute E(X). Hint: use the CDF expectation formula, and integration by parts. You may assume that lim, t"o(-t) 0 for all n 2 0. 4. Find the CDF Fx (u) 5. Compute V(X). Hint: use Fxa, and follow the same hint of part (3) 1. Find Fx in terms of φ (t). Is X a continuous random variable ? 2....
A random variable has a moment generating function given by MX(t) = (e^t + 1)^4/16 . Find the expected value and the variance of the variable Y = 2X + 3