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- Prob. 3 Let X be a random variable with cumulative distribution function (cdf) given by (1-e-x², x ≥ 0 ={1,- x<0 Find the probability that the random variable X falls within one standard deviation of its mean. Fx (x) =arrow_forward1. Let X be a random variable having pdf f(x) = 6x(1 – x) for 0 < I < 1 and 0 elsewhere. Compute the mean and variance of X. 2. Let X1, X2,..., X, be independent random variables having the same distribution as the variable from problem 1, and let X, = (X1+ ·.+Xn). Part a: Compute the mean and variance of X, (your answer will depend on n). Part b: If I didn't assume the variables were independent, would the calculation in part a still work? Or would at least part of it still work? 3. Suppose that X and Y are both independent variables, and that each has mean 2 and variance 3. Compute the mean and variance of XY (for the variance, you may want to start by computing E(X²Y²)). 4. Suppose that (X,Y) is a point which is equally likely to be any of {(0, 1), (3,0), (6, 1), (3, 2)} (meaning, for example, that P(X = 0 and Y = 1) = }). Part a: Show that E(XY) = E(X)E(Y). Part b: Are X and Y independent? Explain. 5. Let X be a random variable having a pdf given by S(2) = 2e-2" for 0arrow_forwardLet rt be a log return. Suppose that r0, r1, . . . are i.i.d. N(0, 0.01^2).(a) What is the distribution of rt(8) = rt + rt−1 + rt−2 +...+ rt−7?(b) What is the covariance between r7(3) and r9(3)?(c) What is the conditional distribution r17(3) given that r16 =0.004 (d) What is the probability that the gross return over the first 10 times periods is at least 1.05?arrow_forwardPart III: Sections 3.1- 3.3 13. Suppose that X is a continuous random variable with pdf given as 43 if 1< <4 255 f(1) = %3D otherwise. (a) For 1arrow_forward6.2 Consider a mixture of two Gaussian distributions (illustrated in Figure 6.2), 0.4 N ([102], [1001]) + 0.6 N ([00], [8.4 2.0 2.0 1.7]). a. Compute the marginal distributions for each dimension. b. Compute the mean, mode and median for each marginal distribution. c. Compute the mean and mode for the two-dimensional distribution.6.2 Consider a mixture of two Gaussian distributions 10 10 0 8.4 2.0 0 2.0 1.7 (illustrated in Figure 6.2), 0.4N([ ].[ 1) +0.6N([ ].[₂ 1). a. Compute the marginal 2 0 1 distributions for each dimension. b. Compute the mean, mode and median for each marginal distribution. c. Compute the mean and mode for the two-dimensional distribution. 6.2 Consider a mixture of two Gaussian distributions (illustrated in Figure 6.2), 8.4 0.4.N N([2] [6]) +0.6N ([8] · [2.6 2.7]). a. Compute the marginal distributions for each dimension. b. Compute the mean, mode and median for each marginal distribution. c. Compute the mean and mode for the two-dimensional distribution. Σ Q 2 :arrow_forward7. Derive the Wigner distribution of the signal s(t) = e- +juntarrow_forward7. Let M(t)=(1/6)et +(2/6)e²t+(3/6)e³t (Moment generating function of X) a. Find E(x) b. Find Variance (x) nisarrow_forwardVisitors come to a two-server location following a Poisson process with a rate of A. When they arrive, they join a common queue. The next person in line begins service as soon as a server finishes servicing the previous customer. The service times of server i are exponential RVs with rate μi, i=1; 2, where μ₁ + #2 > A. An arrival finding both servers free is equally likely to go to either one. 1. (10 pts) Define an appropriate continuous-time Markov chain for modeling this prob- lem. 2. (15 pts) Write flow balance equations for this CTMC 3. (15 pts) Find the limiting probabilities. Show your calculations.arrow_forwardSarrow_forwardSuppose that X₁,..., X, is a random sample from a normal distribution, X,~ N(0, 0). Show that equation (10.2.1) does not depend on 0 if S = [X². ƒ(x₁, ..., x₁; 0) (s; 0) if (x₁,..., x₁) = s £x)(x1. 0 otherwise (10.2.1) Use cdf method to find f(s) Fs(s) = Fs/teta(s/teta) so, f(s)-fs/teta(s/teta) d(s/teta/ds ***arrow_forwardLet X1, .... Xn be a random sample from a population with location pdf f(x-Q). Show that the order statistics, T(X1, ...., Xn) = (X(1), ... X(n)) are a sufficient statistics for Q and no further reduction is possible?arrow_forwardA random variable X has a N(0,1) distribution. Use the moment generating function of X to compute (a) the third and (b) fourth moments of X, i.e., E(X³) and E(X4).arrow_forwardarrow_back_iosSEE MORE QUESTIONSarrow_forward_iosRecommended textbooks for you
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