Problem 2 Consider a random variable with cdf(x) = 1-exp(-10x) when x2 0. And 0 otherwise. a) Determine Pr{ X 54} b) Pr{ Xe (1, 4)} c) Find PDF(x)
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- Repeat Example 5 when microphone A receives the sound 4 seconds before microphone B.Problem 1. A continuous random variable X is defined by f(x)=(3+x)^2/16 -3 ≤ x ≤ -1 =(6-2x^2)/16 -1 ≤ x ≤ 1 =(3-x^2)/16 -1 ≤ x ≤ 3 a)Verify that f(x) is density. b)Find the Mean1)Let x be a random variable Gaussian with zero mean and variance 1. Find:a)The conditional pdf and pdf of x given x > 0;b)E [ x| x>0 ]c)Var [ x | x >0]
- 13) Random variables X and Y have joint pdf fXY={4xy, 0≤x≤1, 0≤y≤1fXY={4xy, 0≤x≤1, 0≤y≤1 Find Correlation and CovarianceQuestion 15. Herpes simplex-2. Suppose 7.5% of a population is infected with Herpes simplex-2 virus (HSV2). You select seven individuals at random from the population. What is the probability of finding at least one HSV2-positive individual in your sample? [Hint: First find Pr(X = 0). Then make use of the fact that (X ≥ 1) = 1− Pr(X = 0).]If X is an exponential random variable with PDF fX( x ) = a exp ( − ax ) for x ≥ 0, where a =0.8. Find P [X>b] if b=.479
- Q1- What is the probability that the number of heads and are equal it a lait coin is tossed, a)15 times b)20 times Q2- Suppose the joint pdf of X and Y is given by: F xy (x y)={ ex +3y, if 0<x< 1 and 0<y<1 0 otherwise find E(x) . Q2- Suppose X is a random variable with E(X) = 5 and Var(X) = 2. What is E(X)?The premise pdf of the random variable X is X ∼ U (0, 2). The pdf of the random variable Y under the condition X Geometric (x).That is,since fY | X (y x) ∼ Geometric (x) and Y = 3 observations. fX | Y (x | 3) =?#1. Suppose that X is a random variable with pdf: f(x) = (2x + 1)/6, 0 < x < 2. (a)Find the cdf . Give the complete cdf for any real value of x. (b)Find the mean and variance of X. (c)Verify that Chebyshev’s Theorem holds for k = 5/3. (d) Find P(0.10 < X < 0.50) using the cdf in (a).
- Problem 7: Let X be a continuous random variable with the probability density for f(x) = 3x2 values of x in [0,1], and f(x) = 0 elsewhere. Compute the expected value and variance of X.1. Define a random variable X as the proportion of time that a student spends on a certain test. Research suggests that the pdf of x is f(x) = (θ + 1)xθ , 0 ≤ x ≤ 1. For the parameter θ, we know θ > −1, but its exact value is still unknown. To estimate the parameter, a random sample of 10 students is obtained, and their proportions of time are: 0.92, 0.79, 0.90, 0.65, 0.86, 0.47, 0.73, 0.97, 0.94, 0.77. Find the Maximum Likelihood Estimator (MLE) of θ.Two random variables X and Y with a joint PDF f(x,y) = Bx+y for 0≤ x ≤6 , 0≤ x ≤7 and constant B Find value of constant B
