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- Let X represent a continuous random variable with a Uniform distribution over the interval from 0 to 2. Find the following probabilities (use 2 decimal places for all answers):(a) P(X ≤ 0.22) = (b) P(X < 0.22) = (c) P(0.98 ≤ X ≤ 1.92) = (d) P(X < 0.98 or X > 1.92) =Let Y be a Poisson random variable with mean λ = 2. (a) Find P(Y ≥ 2). (b) Find P(Y ≥ 4|Y ≥ 2).Assume that X is a random variable whose conditional distribution given the variable Y is poisson P (X | Y) = Po (Y). Suppose further that Y has a gamma distribution Y ∼ Gamma (1, 1). (a) Determine the value E (XY). (b) Determine the conditional distribution P (Y | X).
- Consider the pair of random variables ( X , Y ) with joint PDF f X , Y ( x , y ) = 2 for 0 ≤ x ≤ y ≤ 1 and f X , Y ( x , y ) = 0 otherwise. (In particular, note that the joint PDF is only nonzero when x ≤ y .) (a) Draw a picture of the range of ( X , Y ) in the x y -plane. (b) Describe the conditional distribution of X given Y = y , for 0 ≤ y ≤ 1 . (c) What is the marginal PDF f X of X ? (d) Are X and Y independent?b) calculate var(x) Give your answer to four decimal places.a) Let X₁, X₂, X₁,...,X, be a random sample of size n from population X. Suppose that X-N(0, 1) and Y = -1-8√n. i) Show that the standard score of the sample mean X, is equal to Y. ii) Show that the mean and variance of the random variable Y are 0 and 1, respectively. iii) Show using the moment generating function technique that Y is a standard normal random variable. iv) What is the probability that Y2 is between 0.02 and 5.02?
- A random point (X,Y) is distributed uniformly on the square with vertices (1,1), on the square. (1,-1), (-1, 1), and (-1,-1). That is, the joint pdf is f(x,y) Determine the probabilities of the following events. (a) x² + y² 0 (c) |X+Y| < 2Let X be a point randomly selected from the unit interval [0, 1]. Consider the random variable Y = (1-X)-¹/2 (a) Sketch Y as a function of X. (b) Find and plot the cdf of Y. (c) Derive the pdf of Y. (d) Compute the following probabilities: P(Y > 1), P(3 < Y < 6), P(Y ≤ 10).Let X1, . . . , Xn be independent random variables, such that Xi ∼ Poiss(λi), for i = 1, . . . , n. Find the distribution of Y = X1 + · · · + Xn.
- Let X be a random variable corresponding to 2021 oil production in New Mexico relative to 2020. Let Y be a random variable corresponding to 2021 oil production in Texas relative to 2020. Suppose the joint distribution of X and Y is f (x,y) = 4xy for x,y SET [0,1] Find P (X < 1/2 | Y > 3/4) ** X is less than or equal to 1/2 and Y is greater than or equal to 3/4The distribution of the random variable X is a given by a function:f(x) = 1/c ; 0<x<1, = x/3 ; 1<=x < 2, = 0 ; else Determine the F(x) Count P(X > 1,4)Let X1, . . . , Xn be random variables corresponding to n independent bids for an item on sale. Suppose each Xi is uniformly distributed on [100, 200]. If the seller sells to the highest bidder, what is the expected sale price? A)Find the pdf of W = Max (X1, X2, …, Xn). B) Find E(W). Hint: Let W = Max (X1, X2, …, Xn). 1. P[W ≤ c] = P[Max (X1, X2, …, Xn) ≤ c] = P[X1 ≤ c, X2 ≤ c,…, Xn ≤ c] 2. Obtain the pdf of W by differentiating its cdf of W.