Question

4. A sequence of uniformly distributed random variables are presented by Xi where i = 1...

4. A sequence of uniformly distributed random variables are presented by Xi where i = 1 … 50 on interval [0, 20] and a resultant random variable is created from them as X = (1/50)∑Xi . Another sequence of exponentially distributed random variable are presented by Yj where j = 1… 40 with parameter λ = 0.2, and a resultant variable is created from them as Y = (1/40)∑Yj . Now if Z = X + Y, find P{Z < 16}

Math   Statistics-And-Probability   
0 0
Add a commentTranscribed image text
Next >

Homework Answers

Answer #1

0 0
Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Log In

Sign Up

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
4. A sequence of uniformly distributed random variables are presented by Xi where i = 1...
4. A sequence of uniformly distributed random variables are presented by Xi where i = 1 … 50 on interval [0, 20] and a resultant random variable is created from them as X = (1/50)∑Xi . Another sequence of exponentially distributed random variable are presented by Yj where j = 1… 40 with parameter λ = 0.2, and a resultant variable is created from them as Y = (1/40)∑Yj . Now if Z = X + Y, find P{Z <...
Let X1, X2,... be a sequence of independent random variables distributed exponentially with mean 1. Suppose...
Let X1, X2,... be a sequence of independent random variables distributed exponentially with mean 1. Suppose that N is a random variable, independent of the Xi-s, that has a Poisson distribution with mean λ > 0. What is the expected value of X1 + X2 +···+ XN2? (A) N2 (B) λ + λ2 (C) λ2 (D) 1/λ2
Let Xi, i = 1, 2..., 48, be independent random variables that are uniformly distributed on...
Let Xi, i = 1, 2..., 48, be independent random variables that are uniformly distributed on the interval [-0.5, 0.5]. (a) Find the probability Pr(|X1|) < 0.05 (b) Find the approximate probability P (|Xbar| ≤ 0.05). (c) Determine an approximation of a such that P(Xbar ≤ a) = 0.15
Let U be a random variable that is uniformly distributed on (0; 1), show how to...
Let U be a random variable that is uniformly distributed on (0; 1), show how to use U to generate the following random variables: (a) Bernoulli random variable with parameter p; (b) Binomial random variable with parameter n and p; (c) Geometric random variable with parameter p.
A random variable X is exponentially distributed with an expected value of 77 a-1. What is...
A random variable X is exponentially distributed with an expected value of 77 a-1. What is the rate parameter λ? a-2. What is the standard deviation of X? b. Compute P(68 ≤ X ≤ 86)
Let X and Y be independent random variables following Poisson distributions, each with parameter λ =...
Let X and Y be independent random variables following Poisson distributions, each with parameter λ = 1. Show that the distribution of Z = X + Y is Poisson with parameter λ = 2. using convolution formula
The random variable X is uniformly distributed in the interval [0, α] for some α >...
The random variable X is uniformly distributed in the interval [0, α] for some α > 0. Parameter α is fixed but unknown. In order to estimate α, a random sample X1, X2, . . . , Xn of independent and identically distributed random variables with the same distribution as X is collected, and the maximum value Y = max{X1, X2, ..., Xn} is considered as an estimator of α. (a) Derive the cumulative distribution function of Y . (b)...
Let {Xj} ∞ j=1 be a collection of i.i.d. random variables uniformly distributed on [0, 1]....
Let {Xj} ∞ j=1 be a collection of i.i.d. random variables uniformly distributed on [0, 1]. Let N be a Poisson random variable with mean n, and consider the random points {X1 , . . . , XN }. b. Let 0 < a < b < 1. Let C(a,b) be the number of the points {X1 , . . . , XN } that lies in (a, b). Find the conditional mass function of C(a,b) given that N =...
a) Let Xi for i = 1,2,...n be random variables with E[Xi] = μi (not necessarily...
a) Let Xi for i = 1,2,...n be random variables with E[Xi] = μi (not necessarily independent). Show that E[∑ni =1 Xi] = [∑ni =1 μi]. Show from Definition b) Suppose that random variables Yi for i = 1, 2,...,n are independent and identically distributed withE[Yi] =γ(gamma) and Var[Yi] = σ2, Use part (a) to show that E[Ybar] =γ(gamma). (c) Suppose that random variables Yi for i = 1, 2,...,n are independent and identically distributed with E[Yi] =γ(gamma) and Var[Yi]...
a)—Random variable y is continuously and uniformly distributed between 0 to 1.what is the probability that...
a)—Random variable y is continuously and uniformly distributed between 0 to 1.what is the probability that y =0.33? b)—Random variable X is continuously and uniformly distributed over the range 0 to 7.1. What is σx the standard deviation of x?
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT