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6.2.2 X is the Gaussian (0,1) random var- iable. Find the CDF of Y = |X|...
7.6.2 X and Y are jointly Gaussian ran- dom variables with E[X] and Var[X] = Var[Y] = 1. Furthermore, E[Y X] X/2. Find fx,y(x, y) E[Y] 0
Let X and Y be independent Gaussian(0,1) random variables. Define the random variables R and Θ, by R2=X2+Y2,Θ = tan−1(Y/X).You can think of X and Y as the real and the imaginary part of a signal. Similarly, R2 is its power, Θ is the phase, and R is the magnitude of that signal. (a) Find the joint probability density function of R and Θ, i.e.,fR,Θ(r,θ).
s 9.1.4 X1, X2 and X3 are iid continuous uniform random variables. Random var- iable Y = X1 + X2 + X3 has expected value E[Y] = 0 and variance oy = 4. What is the PDF fx,(x) of Xı?
2X x 20 5 pt. a. Find the cdf and pdf of Y in terms of the cdf and pdf of X. of Y when X is a Gaussian random variable with zero mean and variance-4
7. Let X and Y be independent Gaussian random variables with identical densities N(0,1). Compute the conditional density of the random variable of X given that the sum Z = X + Y is known (i.e., XIX + Y)
6 Suppose that X and Y are random variables such that Var(X)-Var(Y)-2 and Cov(x,y)- 1. Find the value of Var(3.X-Y + 2)
Question 1 X is an Gaussian random variable with mean of -4.1 and CDF F xx). It is known that Fx (2.5) drops to -68.3% of its peak probability. Below what value of X, do we find the probability Fx (X.) reaches to 4.6% of its peak value?
3. (10 pts.) X is a Gaussian random variable with E{X} = 2 and Var(X) = 16. Let Y = 3X +1. Determine the probability: Pr(Y > 2)
6 Suppose that X and Y are random variables such that Var(X) Var(Y)-2 and Cov(x,y)- 1. Find the value of Var(3.X-Y+2)
a. Find the cdi and pdf of Y in terms of the cdf and pdf of X 3 pt. b. Find the pdf of Y when X is a Gaussian random variable with zero mean and unit variance 3 pt.