2-t 2. Suppose that X - Exp(2), for some i >0. We know that the moment...
Suppose that X - Exp(2), for some a > 0. We know that the moment generating function of X is given by M(O)= E[em]= , for some appropriate set of values of t. (a) Derive this mgf result. Explain what condition ont is necessary for this expression to be valid, and why this condition is necessary. (b) Use the mgf to find the first four moments (u, us, and u) of X. (c) Use your results in part (b) to...
10. The moment generating function of the random variable X is given by My(t) = exp{2e* – 2} and that of Y by My(t) = fet +. Assuming that X and Y are independent, find (a) P{X + Y = 2). (b) P{XY = 0}. (c) E(XY).
Examples: Binomial distribution ■ X ~ Exp(β), ■ f(x) ■ F(x) x > 0. E(X) ■ Var(X) ■ Moment generating function and first moment.
The moment generating function (MGF) for a certain probability distribution is given by 2 (2 + 2) , M(t) = R. t 2 Suppose Xi, X2, are iid random variables with this distribution. Let Sn -Xi+ (a) Show that Var(X) =3/2, i = 1,2. (b) Give the MGF of Sn/v3n/2. (c) Evaluate the limit of the MGF in (b) for n → 0. The moment generating function (MGF) for a certain probability distribution is given by 2 (2 + 2)...
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).
6. (4 marks) The moment generating function (mgf) of a random variable X is given by m(t)-e2 (a) Use the mgf to find the mean and variance of X (b) What is the probability that X-2?
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...
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).
Random variable X has MGF(moment generating function) gX(t) = , t < 1. Then for random variable Y = aX, some constant a > 0, what is the MGF for Y ? What is the mean and variance for Y ?
(п-1)S? for the conditional 1-3) Show that the moment generating function(MGF) of distribution of 2,given X is (n-1)S2 | X (1-2 -(n-l)/2 ,1 < 2 E expt Hint: Notice that g,,, is a pdf That is, 7 1- "ppxp )./ (n-1)S2 X Еl exp| t in a multi-integral form using the conditional pdf of Express X2,, given X Then try to consider the integrand as another joint pdf times a constant. Then the answer will be the constant. Hint (п-1)S?...