The MGF of Poisson variate is of the form
This implies that
Also, for a Poisson variate,
a)
Mean(X) = 3
b)
Mean(Y) = 4
c)
Mean(Z) = 7
d)
Var(X) = 3
e)
Var(Y) = 4
f)
Var(Z) = 7
Consider these three moment generating functions, for X, Y and Z: (5 points each) m (t)=W-3...
5 (10 points) X and Y are independent random variables with common moment generating function M(t) eT. Let W X + Y and Z X - Y. Determine the joint moment generating function, M(ti, t2) of W and Z Find the moment generating function of W and Z, respectively
3. (4 points) The random variables X and Y are independent and have moment generating functions Find Var(X).x (t) =er-2t and Mr(t)=e3t2+tid t a) Find MGF of Z Find Var(Z). Find joint MGF of X and Z, i.e. Mxz(t1,t2) 2X-Y c) d)
The moment generating function ф(t) of random variable X is defined for all values of t by et*p(x), if X is discrete e f (x)dx, if X is continus (a) Find the moment generating function of a Binomial random variable X with parameters n (the total number of trials) and p (the probability of success). (b) If X and Y are independent Binomial random variables with parameters (n1 p) and (n2, p), respectively, then what is the distribution of X...
Let X and Y be independent random variables, with known moment generang functions Mx(t) and My (t) and Z be such that P(Z = 1) = 1-P(Z 0) = p E (0,1). Compute the moment generating function of the random variable S- ZX (1 - Z)Y. [The distribution of S is called a mirture of the distributions of X and Y.] Your answer can be left in terms of Mx(t) and My (t) Hint: If you don't know how/where to...
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...
0.4. Suppose that Yi and Y2 are discrete independent random variables with the following moment generating functions: 6 10 102 I. Find the mean and variance of Yi 1- 0.4. Suppose that Yi and Y2 are discrete independent random variables with the following moment generating functions: 6 10 102 I. Find the mean and variance of Yi 1-
(6) (15 points) The moment generating function for a normal random variable N (17,0?) is given by M(t) =e(+rt). Given Y with pdf N (4,0%), show that, if X and Y are independent, then the random variable 2 = x + Y is normally distributed with variance o + oz and mean 41 + 12. Please state clearly which properties of the moment generating function you are using.
Let be a random variable with probability density function f(x) and moment-generating function 1 1 M(t) = =+ = ? 6 . 6 1 + - 1 36 + -e a) Calculate the mean = E(X) of X b) Calculate the variance o? = E(X -w' and the standard deviation of X
X and Y are two independent and identical random variables with moment generating function M(t) You are given: M'(0)= 4 M"(0)32 Calculate the absolute value of the coefficient of variation of X - 2Y.
2. Let Xand Y be random variables with joint moment generating function M(s,t) 0.3+0.1es + 0.4e +0.2 es*t (a) What are E(X) and E(Y)? (b) Find Cov(X,Y) 2. Let Xand Y be random variables with joint moment generating function M(s,t) 0.3+0.1es + 0.4e +0.2 es*t (a) What are E(X) and E(Y)? (b) Find Cov(X,Y)