We will first evaluate the possible values of Y using the given function. Then we will attach the probabilities to the values of Y. After that we will compute the expectation of Y.
1. Let X be a discrete rv with possible values(-1,0,1,2), each with probability %. Let g(x)...
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
5. Let X be a discrete random variable. The following table shows its possible values associated probabilities P(X)( and the f(x) 2/8 3/8 2/8 1/8 (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. (d) Compute the mean and the variance of X.
1) [15 pts.] Let Z be a discrete random variable having possible values 0, 1,2, and 3 and probability mass function p(0)-1/4, p(1) =1/2, p(2)-1/8, p(3) =1/8. (a) Plot the corresponding (cumulative) distribution function. (b) Determine the mean ETZ. (e) Evaluate the variance Var(Z)
Determine whether the random variable is discrete or continuous. In each case, state the possible values of the random variable. (a) The number of points scored during a basketball game. (b) The square footage of a house. (a) Is the number of points scored during a basketball game discrete or continuous? O A. The random variable is continuous. The possible values are x = 0, 1, 2,.... OB. The random variable is continuous. The possible values are x20. O C....
The table below shows the probability distribution of a discrete random variable X. Values of the random variable X (x) Probability of observing each value of X P(X = x) 6 0.20 7 0.25 8 0.25 9 0.10 10 0.12 11 0.08 Total 1.00 (a) Determine the probability that the random variable X is between 8 and 10, inclusive. (1 mark (b) Determine the probability that the random variable X is at least 9. (1 mark) c. Determine the probability...
Problem 4 Let X be a discrete random variable with probability mass function fx(x), and let t be a function. Define Y = t(X): that is, Y is the randon variable obtained by applying the function t to the value of X Transforming a random variable in this way is frequently done in statistics. In what follows, let R(X) denote the possible values of X and let R(Y) denote the possible values of To compute E[Y], we could irst find...
Determine whether the random variable is discrete or continuous. In each case, state the possible values of the random variable. (a) The number of points scored during a basketball game. (b) The amount of rain in City B during April. (a) Is the number of points scored during a basketball game discrete or continuous? A. The random variable is discrete. The possible values are x≥0. B. The random variable is continuous. The possible values are x =0, 1, 2..... C. The random variable is discrete. The...
Determine whether the random variable is discrete or continuous. In each case, state the possible values of the random variable. (a) The number of customers arriving at a bank between noon and 1 : 00 P.M.. (b) The distance a baseball travels in the air after being hit. (a) Is the number of customers arriving at a bank between noon and 1 : 00 P.M. discrete or continuous? A. The random variable is discrete. The possible values are x greater...
Determine whether the random variable is discrete or continuous. In each case, state the possible values of the random variable. (a) The number of points scored during a basketball game. (b) The time it takes for a light bulb to burn out. (a) is the number of points scored during a basketball game discrete or continuous ? O A. The random variable is continuous. The possible values are x 20. OB. The random variable is discrete. The possible values are...
3. Let X be a discrete random variable with the probability mass function 2 18 x=1,2,3,4, zero otherwise. , 12 a Find the probability distribution of Y-g(X- TXI b) Does Hy equal to g(Hx)? Ax=E(X), μ,-E(Y).