21 (1 point) If Y X and every Xi is i.i.d with a chi-squared distribution with...
(1 point) If Y-Σǐn 1 X, and every Xi is i.i.d with distribution binomial(n, p), find the MGF of Y M(t) What is the distribution of Y? Select all that apply. There may be more than one correct answer A. binomial (n, m * p) B. negative binomial(m * n,p) C. negative binomial(n,p) D. negative binomial(m, p) E. binomial(m,p) F. binomial(n,p) G. binomial(m * n,p) H. negative binomial(n,m* p) I. None of the above
PROBLEM 8: It is easy to show with mgf's that the sum of independent chi-squared random variables has a chi- squared distribution with degrees of freedom equal to the sum of the degrees of freedom associated with each of the random variables being summed. For example, if w (df -i) and all W's are independent, then Σ W,-X2 | df n(n+1 2 서 Now, suppose that X, N(i,i), and all X's are independent. Using your result in the previous problem...
1 point) If YX and every X, is i.i.d with distribution binomial(n, p), find the MGF of Y M(t) = What is the distribution of Y? Select all that apply. There may be more than one correct answer. DA, binomial(rn * n, p) B. binomial(n, m*p) | | C. binomial(m, p) D. negative binomial(n,p) E. negative binomial(m,p) F. negative binomial(n, m* p) G. binomialn,p) OH. negative binomial(m * n, p) I. None of the above
Having troubles with question 2. Please help 2. If X has a Gamma distribution with parameters a and B, then its mgf is given by (a) Obtain expressions for the moment-genérating functions of an exponential random variable and of a chi-square random variable by recognizing that these are special cases of a Gamma distribution and using the mgf given above. (b) Suppose that X1 is a Gamma variable with parameters α1 and β, X2 is a Gamma variable with parameters...
9. Show that Y-Σ|-1 z? has a Chi-squared distribution with n degrees of freedom, where Z's are iid rvs from a standard normal distribution. (Prove it mathematically)
Let Y~ xî (i.e. Y follows a chi-squared with 1 degree of freedom), let Xn = n-1/2 y - n1/2 (a) Show that Xn 4 X where X ~ N(0,2) (Hint: look up what is the mean and variance of a chi-squared distribution?) (b) n= 30. Find the exact P(Y > 43.8) using a chi-squared table. (c) Approximate P(Y > 43.8) for n = 30 using a normal approximation
(a) Let x, have a chi-squared distribution with parameter y, and let x, be independent of x, and have a chi-squared distribution with parameter vz Show that x2 + x, has a chi-squared distribution with parameter v, + vz Let y = x2 + x2. Identify the correct expression for Fyn). "1/2 - 2x212/2-12-(x1 + x2)/2 dx1 OFyly) = ' -X1/2 dx1 + -x212 dxz e 109) = 6 {1*(***) ** (*)*" + x2)120mg +6 (277(3)) -<*****@*)*** .(-)70%as C (7-(1)...
(a) Let X, have a chi-squared distribution with parameter V, and let X, be independent of X, and have a chi-squared distribution with parameter vz. Show that X, + X, has a chi-squared distribution with parameter v, + V Let Y = X1 + Xy. Identify the correct expression for Fly). Fyly) = (f1 +49 (0) BM={{1*(**) 2*3)**- _jei OFW - 1 -{{49)..:@) ib) dx1 FY) = -xq12 dx + -*2/20 1²ax 2 1/2 O Fy(y) :{"(****): 19). x 22...
(1 point) If Y-LX, and every X, is ii.d with distribution binomial(n, p), find the MGF of Y M(t) What is the distribution of Y? Select all that apply. There may be more than one correct answer A. binomial(n, m p) B. negative binomial (n, p) C. negative binomial(m,p) D. binomial (n, p) E. negative binomial(n, m * p) F. binomial(m *n,p) G. binomial(m, p) OH. negative binomial(m * n, p) I. None of the above (1 point) If Y-LX,...
4. Let 8 >0. Let X, X2,..., X, be a random sample from the distribution with probability density function S(*;ð) - ma t?e-vor x>0, zero otherwise. Recall: W=vX has Gamma( a -6, 0-ta) distribution. Y=ZVX; = Z W; has a Gamma ( a =6n, = ta) distribution. i=1 E(Xk) - I( 2k+6) 120 ok k>-3. 42 S. A method of moments estimator of 8 is 42.n 8 = h) Suggest a confidence interval for 8 with (1 - 0) 100%...