Consider a Bernoulli process with μ=0.3. Simulate a sequence of 100 outcomes. Interpret the simulated sequence as follows: placea walker at position 0. A '0' is interpreted as a move of size 0, a '1' is interpreted as a move of size 1. Our simulated sequence canthus be interpreted as a random walk. Record the end position of our random walk. Repeat this 10000 times. Histogram these endpositions. Your histogram, when appropriately binned, should resemble a Gaussian. Give u and of this distribution. If you areconfident, you may also use the central limit theorem directly and dispense with simulation.Bμ=20,a=8.4μ=20,a=11H=30,0=4.6μ=30,0=2.8

We are given a Bernoulli process with mean μ=0.3 We will use Central limit theorem here.

Answered: Consider a Bernoulli process with =0.3.…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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This cumulative review problem uses material from Chapters 3, 5, and 10. Recall that the Poisson distribution deals with rare events. Death from the kick of a horse is a rare event, even in the Prussian army. The following data are a classic example of a Poisson application to rare events. The data represent the number of deaths from the kick of a horse per army corps per year for 10 Prussian army corps over a period of time. Let x represent the number of deaths and f the frequency of x deaths. x 0 1 2 3 or more f 113 62 21 4 (a) First, we fit the data to a Poisson distribution.The Poission distribution states P(x) = e−??x x! , where ? ≈ x (sample mean of x values). From our study of weighted averages, we get the following. (b) Now we have P(x) = e−0.58(0.58)x x! for x = 0, 1, 2, 3 . Find P(0), P(1), P(2), and P(3 ≤ x). Round to three places after the decimal. P(0) = P(1) = P(2) = P(3 ≤ x) = (c) The total number of…
Question 11 Log reports show that a piece of software is crashing on average twice in every 24 hour period. Using the Poisson distribution find the probability that the software will crash a. Exactly twice in the next 24 hours. b. More than four times in the next 24 hours. C. Exactly three times in the next week.
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Question 15 You want to measure traffic on the Lake Lewisville Toll bridge during the late evening. Select all the discrete random variables (there may be more than one). The number of cars that use the bridge in one hour. The time between each car crossing the bridge. The time each car crosses the bridge. The total number of cars that cross the bridge. Submit Question Search €0 W
This cumulative review problem uses material from Chapters 3, 5, and 10. Recall that the Poisson distribution deals with rare events. Death from the kick of a horse is a rare event, even in the Prussian army. The following data are a classic example of a Poisson application to rare events. The data represent the number of deaths from the kick of a horse per army corps per year for 10 Prussian army corps over a period of time. Let x represent the number of deaths and f the frequency of x deaths. x 0 1 2 3 or more f 111 61 24 4 (a) First, we fit the data to a Poisson distribution.The Poission distribution states P(x) = e−λλx x! , where λ ≈ x (sample mean of x values). From our study of weighted averages, we get the following. x = Σxf Σf Verify that x = 0.605. Hint: For the category 3 or more, use 3. x = (b) Now we have P(x) = e−0.605(0.605)x x! for x = 0, 1, 2, 3 . Find P(0), P(1), P(2), and P(3 ≤ x). Round to three places…

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