a) Find the marginal distribution of X and Y b) What is the conditional distribution of X given Y=2. c) Compute Cov (X,Y) d) Are X and Y independent?
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- Suppose that the random variable X is continuous and takes its values uniformly over the interval from 0 to 2. What is P{X = 1.5 or X = 0.4}?If X1, X2, ... , Xn are independent random variables having identical Bernoulli distributions with the param-eter θ, then X is the proportion of successes in n trials, which we denote by ˆ . Verify that(a) E()ˆ = θ;(b) var()ˆ = θ (1 − θ )n .Suppose a random variable Y uniformly distributed over the interval [2, 7]. 1. What is f(y)?
- Suppose the random variable y is a function of several independent random variables, say x1,x2,...,xn. On first order approximation, which of the following is TRUE in general?Assume I observe 3 data points x1, x2, and x3 drawn independently from anunknown distribution. Given a model M, I can calculate the likelihood for each data point as Pr(x1 | M) = 0.5, Pr(x2 | M) = 0.1, and Pr(x3 | M) = 0.2. What is thelikelihood of seeing all of these data points, given the model M: Pr(x1, x2, x3 | M)?Let X1 and X2 be observations of a random sample of size n = 2 from a Cauchy Distribution.Find P(X1 < −1 and 1 < X2)
- There are two traffic lights on a commuter's route to and from work. Let X1 be the number of lights at which the commuter must stop on his way to work, and X2 be the number of lights at which he must stop when returning from work. Suppose that these two variables are independent, each with the pmf given in the accompanying table (so X1, X2 is a random sample of size n = 2). x1 0 1 2 p(x1) 0.1 0.2 0.7 ? = 1.6, ?2 = 0.44 (a) Determine the pmf of To = X1 + X2. to 0 1 2 3 4 p(to) (b) Calculate ?To. ?To = How does it relate to ?, the population mean? ?To = · ? (c) Calculate ?To2. ?To2 = How does it relate to ?2, the population variance? ?To2 = · ?2If X is a continuous random variable with X ∼ Uniform([0, 2]), what is E[X^3]?An alternative proof of Theorem 2 may be based on the fact that if X1, X2, ..., and Xn are independent ran-dom variables having the same Bernoulli distribution with the parameter θ, then Y = X1 + X2 +···+ Xn isa random variable having the binomial distribution withthe parameters n and θ.Verify directly (that is, without making use of the factthat the Bernoulli distribution is a special case of thebinomial distribution) that the mean and the variance ofthe Bernoulli distribution are μ = θ and σ2 = θ (1 − θ