Let X₁, X2, X3 be independent & identically distributed standard normal random variables and let Y₁ ~ N(1,4) and Y₂ ~ N(0.9) where Y, and Y₂ are independent & also independent of the Xis, i=1,2,3. a) Give the joint pdf of X₁, X₂, X3 b.) What is the variance of ×? c.) Compute the covariance between X₁ X₂ +3X3 and 2Y₁ + Y3 d). Compute the X₁ X₂ + 3X3 correlation coefficient between and 2Y₁ + Y3.
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- Let X1 and X2 be independent chi-square random variables with r1 and r2 degrees of freedom, respectively. Let Y1=(X1/r1)/(X2/r2) and Y2=X2. (a) Find the joint pdf of Y1 and Y2.Let the random variable X be defined on the support set (1,2) with pdf fX(x) = (4/15)x3, Find the variance of X.Suppose that Z1, Z2, . . . , Zn are statistically independent random variables. Define Y as the sum of squares of these random variables
- Let X1,...,Xn be iid random variables with expected value 0, variance 1, and covariance Cov [Xi,Xj] = ρ, for i≠j. Use Theorem of linearity of expectation to find the expected value and variance of the sum Y = X1 +...+Xn.Assume Z1, Z2, . . . , Zn are independent standard normal random variables. The random variable Y defined byLet X1, ..., Xn be a random sample from N(μ, σ2), where σ2is known.a) Show that Y = (X1 + X2)/2 is an unbiased estimator of μ.b) Find the Cramer-Rao lower bound for the variance of an unbiasedestimator of μ for a general n.c) What is the efficiency of Y in part (a) above?
- Consider X₁, X₂, . . . , Xn to be independent random variables from a Normal(μ,σ ² ) where both parameters are unknown.Suppose that the random variables X1,...,Xn form a random sample of size n from the uniform distribution on the interval [0, 1]. Let Y1 = min{X1,. . .,Xn}, and let Yn = max{X1,...,Xn}. Find E(Y1) and E(Yn).Let Xi and Yi be random variables with Var(Xi) = σx2 and Var(Yi) = σy2 for all i ∈ {1, . . . , n}. Assume that each pair (Xi, Yi) has correlation Corr(Xi, Yi) = ρ, but that (Xi,Yi) and (Xj,Yj) are independent for all i ̸= j. (a) What is Cov(Xi,Yi) in terms of σx, σy and ρ? (b) Show that Cov(Xi,Y ̄) = (ρσxσy)/n, where Y ̄ is the average of the Yi (c) Determine Cov(X ̄,Y ̄). B2. Consider the random variables Xi and Yi from question B1 again. (a) Show that the sample covariance is an unbiased estimator of Cov(X1,Y1). Hint: consider the equality Xi − X ̄ = (Xi − μ) − (X ̄ − μ). (b) Can you conclude from the statement in part (a) that the sample correlation is an unbiased estimator of Corr(X1, Y1)? Justify your answer.
- Suppose that the continuous two-dimensional random variable (X, Y ) is uniformly distributed over the square whose vertices are (1, 0), (0, 1), (−1, 0), and (0, −1). Find the Correlation Coefficient ρxyLet X1, X2, ... , Xn be a random sample, normally distributed with mean μ and variance σ2If σ2 is unknown, find a minimum value for n to guarantee, with probability 0.90, that a 0.95 CI for μ will have length no more than σ/4 explain.X is an exponential random variable with λ =1 and Y is a uniform random variable defined on (0, 2). If X and Y are independent, find the PDF of Z = X-Y2