Examples: Binomial distribution ■ X ~ Exp(β), ■ f(x) ■ F(x) x > 0. E(X) ■...
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...
1. A binomial random variable has the moment generating function, (t) E(etx)II1 E(etX) (pet+1-p)". Show that EX] = np and Var(X) = np(1-p) using that EX] = ψ(0) and E(X2] = ψ"(0). 2. Lex X be uniformly distributed over (a,b). Show that E[xt and Var(X) using the first and second moments of this random variable where the pdf of X is f(x). Note that the nth moment of a continuous random variable is defined as EXj-Γοχ"f(x)dx (b-a)2 exp 2
problems binomial random, veriable has the moment generating function, y(t)=E eux 1. A nd+ 1-p)n. Show that EIX|-np and Var(X) np(1-p) using that EIX)-v(0) nd E.X2 =ψ (0). 2. Lex X be uniformly distributed over (a b). Show that ElXI 쌓 and Var(X) = (b and second moments of this random variable where the pdf of X is (x)N of a continuous randonn variable is defined as E[X"-广.nf(z)dz. )a using the first Note that the nth moment 3. Show that...
Problems binomial random variable has the moment generating function ψ(t)-E( ur,+1-P)". Show, that EIX) np and Var(X)-np(1-P) using that EXI-v(0) and Elr_ 2. Lex X be uniformly distributed over (a b). Show that EX]- and Varm-ftT using the first and second moments of this random variable where the pdf of X is () Note that the nth i of a continuous random variable is defined as E (X%二z"f(z)dz. (z-p?expl- ]dr. ơ, Hint./ udv-w-frdu and r.e-//agu-VE. 3. Show that 4 The...
9. Let a random variable X follow the distribution with pdf f(z)=(0 otherwise (a) Find the moment generating function for X (b) Use the moment generating function to find E(X) and Var(X)
C. (Theory) • Prove that if X Exp(x) for some > 0, ² = Var(x) = 1 / 2
> + x 0. x)e-0, f(x) = fall + x)e-0x tu function. That is, shou is, show that > where (a) Show that f(x) is a density fun What is f(x) > 0, and that bo f(x) dr = 1. (b) Find ELI (c) Find Var(37)
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...
A continuous random variable Y has density function f(y) = f'(y) = 2 · exp[-4. [y] defined for -00 < y < 0. Evaluate the cumulative distribution function for Y Consider W = |Y| and find its C.D.F. and density Determine expected value, E [Y] Derive variance, Var [Y]
6. Let X have exponential density f(x) = le-Az if x > 0, f(x) = 0 otherwise (>0). Compute the moment-generating function of X.