6.2-1. Sample functions in a discrete random process are constants, that is X(t) = C =...
2. A discrete random variable X can be 2, 8, 10 and 20 and its probabilities are 0.3, 0.4, 0.1 and 0.2, respectively. Drive the inverse-transform algorithm for the distribution. 2. A discrete random variable X can be 2, 8, 10 and 20 and its probabilities are 0.3, 0.4, 0.1 and 0.2, respectively. Drive the inverse-transform algorithm for the distribution
Let X be a discrete random variable with probability function f(x). Prove that E[a + b g(X) + c h(X)] = a + bE[g(X)] + cE[h(X))], where g and h are functions, and a, b and c are constants.
1) 2) 3) 4) 5) Suppose that X is a uniform random variable on the interval (0, 1) and let Y = 1/X. a. Give the smallest interval in which Y is guaranteed to be. Enter -Inf or Inf for – or o. Interval:( b. Compute the probability density function of Y on this interval. fy(y) = Suppose that X ~ Bin(4, 1/3). Find the probability mass function of Y = (X – 2)2. a. List all possible values that...
The moment generating function ф(t) of random variable X is defined for all values of t by et*p(x), if X is discrete e f (x)dx, if X is continus (a) Find the moment generating function of a Binomial random variable X with parameters n (the total number of trials) and p (the probability of success). (b) If X and Y are independent Binomial random variables with parameters (n1 p) and (n2, p), respectively, then what is the distribution of X...
A discrete random variable X has probability mass function P() 0.1 0.2 0.2 0.2 0.3 Use the inverse transform method to generate a random sample of size from the distribution of X. Construct a relative frequency table and compare the empirical with the theoretical probabilities. Repeat using the R sample function. 1000
A discrete random variable ? has the sample space ?x = {1,2,3}, with given probabilities of ?x(1) = 0.3, ?x(2) = 0.4, and ?x(3) = 0.3. Compute the expectation ?[(? − ?)2]
Problem 1. (6pt) A discrete random variable X can take one of three different values z1, z and z probabilities ¼, ½ and ¼ respectively, and another random variable Y can 1. 32 and ys, also with probabilities 4V2 and /4, respectively, as shown in the the relative frequency with which some of those values are jointly taken is also shown in the following table with take one of three distinct values P2 P14 (a) (Spt) From the data given...
5. Let X be a discrete random variable. The following table shows its possible values r and the associated probabilities P(X -f(x) 013 (a) Verify that f(x) is a probability mass function (b) Calculate P(X < 1), P(X < 1), and P(X < 0.5 or X > 2). (c) Find the cumulative distribution function of X ompute the mean and the variance of
L.1) Generating functions and discrete random variables a) The data set is X-0, 1, 2, 2, 3, 3, 3 What is et* ? b) The data set is X-0, 1, 2, 2, 3, 3, 3) Give a formula for the generating function of X. c) How is the generating function of X related to ExpectTe]? L.2) Generating functions and discrete random variables a) The random variable is a pull from (0, 1, 2, 2, 3, 3, 3 Give a formula...
1 3 4 9. Suppose the discrete random variables X and Y are jointly distributed according to the following table: Yl-1 0 1 0.1 0.1 0.1 0 0.2 0.1 0.2 0.1 0.1 a. Compute the expected values E(X) and E(Y), variances V(X) and V(Y), and covariance Cov(X,Y) of X and Y. (11) b. Let W = X - Y. Compute E(W) and V(W). [4] 10. Let X be a continuous random variable with probability density function h(x) ce* r >...