Kristi u r correct theta1 is 0
∴ MGF = (e^(t*theta) - 1 )/(t*theta)
let theta =θ
mean = firat derivative of MGF at t=0
Variance = (second derivative of MGF at t=0) - (mean^2)
2. Consider the Poisson distribution, which has a pdf defined as: a) Derive the moment generating function. b) Use the moment generating function and the method of moments to find the mean and the variance. c) If X follows the Poisson distribution with Xx - 2.3, and Y follows a Poisson distribution with XY-54, what is the distribution of the sum X + Y, assuming that X and Y are independent?
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...
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.
5. Find the moment generating function of the continuous random variable X whose a. probability density is given by )-3 or 36 0 elsewhere find the values of μ and σ2. b, Let X have an exponential distribution with a mean of θ = 15 . Compute a. 6. P(10 < X <20); b. P(X>20), c. P(X>30X > 10), the variance and the moment generating function of x. d.
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...
If E(Xr) = 6, r = 1,2,3, , find the moment generating function M(t) of X ạnd the pmf, the mean, and the variance of X ( M(t)-Σ000 M !(0) origin) rk, and note that Mrk, (0) = ElXkl is the kth moment of X about the
4. The moment generating function of the normal distribution with parameters μ and σ2 is (t) exp ( μ1+ σ2t2 ) for -oo < t oo. Show that E X)-ψ(0)-μ and Var(X)-ψ"(0)-[ty(0)12-σ2. 5. Suppose that X1, X2, and X3 are independent random variables such that E[X]0 and ElX 1 for i-12,3. Find the value of E[LX? (2X1 X3)2] 6. Suppose that X and Y are random variables such that Var(X)-Var(Y)-2 and Cov(X, Y)- 1. Find the value of Var(3X -...
2-t 2. Suppose that X - Exp(2), for some i >0. We know that the moment generating function of X is given by M(t)= E[e"]=-4, 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 u , and ) of X. (c) Use your results in part...
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
Statistics: find estimation of parameters k and theta for Gamma distribution using moment generating function method (what are the "method of moments estimators" of k and theta?). Show the proof.