8. Suppose \( X \) has a binomial-(100, 1/50) distribution and \( Y \) has a Poisson distribution with parameter \( \lambda \)?(a) Compute \( P\{X = k\} \).(b) Compute \( P\{Y = k\} \).(c) What should \( \lambda \) be so that \( P\{X = k\} \approx P\{Y = k\} \)?(d) Compute \( P\{X = k\} \) and \( P\{Y = k\} \) for \( k = 0, 1, 2 \). Does the Poisson distribution with your choice of \( \lambda \) appear to be a good approximation of the binomial distribution for those three cases?(e) What is the error and relative error of approximating \( P\{X = 1\} \) by \( P\{Y = 1\} \)?

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Answered: 8. Suppose X has a binomial-(100, 1/50)…

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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Q 4.4. An individual picked at random from a population has a propensity to have accidents that is modelled by a random variable Y having the gamma distribution with shape parameter a and rate parameter 3. Given Y = y, the number of accidents that the individual suffers in years 1, 2, ..., n are independent random variables X₁, X2,... Xn each having the Poisson distribution with parameter y. (a) Write down a function f so that the joint distribution of Y, X₁,..., Xn can be described via =ff(y, k₁, k₂.. f(y, k₁, k2, ... kn)dy P(a ≤ Y ≤ b, X₁ = k₁, X2 = k2 … . . Xn = kn) = [. .. and derive from this expression that, for your choice of f, Y has the Gamma distribution, and that conditionally on Y = y, X₁, X2,... Xn are independent, each having the Poisson distribution with parameter y. (b) Find the conditional distribution of Y given that X₁ = k₁, X2 = k2, . . . , kn.
= 16. A simple random sample of size n = 64 is obtained from a population with u = 77 and o = (a) Describe the sampling distribution of x. (b) What is P (x>79.5) ? (c) What is P (xs72.9)? (d) What is P (75.9 79.5) = (Round to four decimal places as needed.) (c) P (xs72.9) = (Round to four decimal places as needed.) (d) P (75.9
3.- In 1910, E. Rutherford and H. Geiger described in a scientific publication, the results of experiments with polonium. The experiments indicated that the number of a (alpha) particles that reach a small screen during an 8-minute interval has a Poisson distribution with parameter 2 = 3.87. Determine the probability that, during an 8-minute interval, the number, Y, of a particles that reach the screen is b). P(3
Q 4.4. An individual picked at random from a population has a propensity to have accidents that is modelled by a random variable Y having the gamma distribution with shape parameter a and rate parameter ß. Given Y = y, the number of accidents that the individual suffers in years 1, 2, ..., n are independent random variables X₁, X2,... Xn each having the Poisson distribution with parameter y. (a) Write down a function f so that the joint distribution of Y, X₁, Xn can be described via P(a ≤ Y ≤ b, X₁ = k1, X₂ = k2 … . . Xn = kn) = [° ƒ (y, k1, k2, ... kn)dy and derive from this expression that, for your choice of f, Y has the Gamma distribution, and that conditionally on Y = y, X₁, X2,... Xn are independent, each having the Poisson distribution with parameter y.
3.3.24. Let X1, X2 be two independent random variables having gamma distribu- tions with parameters a₁ = 3, B₁ = 3 and a2 = 5, B2 = 1, respectively. (a) Find the mgf of Y = 2X₁ +6X2. (b) What is the distribution of Y?
4. A particle moves on the x- axis starting at x = 0. With equal probability, the particle moves to the left or right one unit. Let X denote the position on the x- axis after four moves. (a) Find px(x). Notice that X can take on -4,-2,0, 2, 4. Fill out the table below. xPx(x) (b) What is the expected position of the particle after four moves. That is, compute E(X). Express your answer as a fraction in lowest terms.
5. Let (*1, x2, .., rn) be n independent random sample of size n from a uniform distribution on the interval [01,02]; (a) find the c.d.f of, X(1), the minimum order statistic (b) find the c.d.f of, X(n), the maximum order statistic (c) Hence, find the mean and variance of X(1) and X(n)-
Q 6.1. Suppose Z = (Z1, Z2, Z3) is a standard multi-variate Gaussian random variable i.e., for i ≤ 3, Zi~ N(0, 1) are i.i.d. random variables. Each of the random variables, (a)-(d), on the left is equal in distribution to exactly one random variables, (1)-(4), on the right. Pair up according to "equal in distribution" and explain briefly your reasoning. (a) (X₂) (¹) (X) = (2) 1/√2 (Ⓒ) (x₂) - (3/1² ¹1/1²) (2) (c = 2 0 = 2 Z₁ 1) (²) 3 (4¹) (X) = (²¹) (1) (2) (3) Y₁ 1 (29) - ()) (2) = Y₂ 1 Y₁ (121) = (1/² √₂) (2) (1/√2 1/√2 /2 −1/√√2, Y₂ X₁ X₂ = 22 1 1 2 0 Y₁ 2 1 (4) (2)) = (²) (²) 1 1 Z₁ Z₂ Z3
5. Let X₁ and X2 be independent random variables. Suppose that X₁ follows a Poisson distribution with parameter X₁, and that X2 follows a Poisson distribution with parameter λ₂. Let Y = X₁ + X₂. (a) Use the MGF technique to find the distribution of Y. (b) Find E(Y) and Var(Y).

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