Section 3.6: Generation of Discrete Random Variables3.78. Octave provides function calls to evaluate the pmf of important discrete random vari-ables. For example, the function Poisson_pdf(x, lambda) computes the pmf at x for thePoisson random variable.(a) Plot the Poisson pmf for λ = 0.5, 5, 50, as well as P[X ≤ k] and P[X > k].

Octave code to plot the Poisson pmf for λ = 0.5, 5, and 50, as well as P[X ≤ k] and P[X > k]: %…

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A First Course in Probability (10th Edition)

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Chapter1: Combinatorial Analysis

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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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Q4\Let a discrete random variable X has values 1, 2, and 3, with probabilities respectively. a) What is PDF for X? b) Sketch the PDF Fx(x) of X. c) Find (i) P(x 2)
Suppose that Y₁, Y₂, ..., Ym is a random sample of size m from Gamma (a = 3, B = 0), where 0 is not known. Check whether or not the maximum likelihood estimator Ô is a minimum variance unbiased estimator of the parameter 8.
5 Calculate XkYk for the n = 5 data points (×₁,Y₁ ) = (2,4), (×2,Y₂) = (3,5), (×3,Y3) = (4,8), (×4,Y4) = (5,10), and k=1 (X5,5) = (6,14). 5 Σ xxYk = [ k=1 (Simplify your answer.)
1a) Derive a maximum-liklihood estimator for the unknown parameter Y 1b) An experienced sales person completes sales following a Poisson distribution, with a mean rate of Y = 1.8 sales/month. A junior sales person completes sales at a mean rate of Y = 0.85 sales per month Find the probability of the joint sales team (experienced and junior) completing exactly 2 sales in any given months, assuming the two sales people act independently of each other. 1c) Find the probability of the joint sales team (experienced and junior) completing more than 2 sales in any given months
EXERCISE 7. Let X be a random variable, for each real number t, define P(t) = E(e*). The function @(t) is called the moment generating function of X. Please try to give the moment generating function of the Poisson distribution with mean 2.
Consider a random variable V modeling the version of the software that people have installed on their computers, where the possible versions are {1, 2, 3, 4}. People are equally likely to have versions 1 and 2 and also equally likely to have versions 3 and 4. People are also x times more likely to have version 3 than version 2. (a) What is the (probability mass function) PMF of V as a function of x (b) What is the expected version of the software. Hint: the expected value of V as a function of x (c) What is the minimum value of x to ensure that the mean version is at least version 3?
Exercise 3. If X and Y are independent Poisson random variables with parameters ui and u2. Prove that W = X +Y_is Poisson with parameter
Let (Ω, Pr) be a probability space, and let X and Y be two independent random variables that are positive and have non-zero variance. (a) Prove that X^2 and Y are independent. Note that by symmetry, it will also follow that Y^2 and X are independent. (b) Use the result from part (a) to show that the random variables W = X + Y and Z = XY are positively correlated (i.e. Cov(W, Z) > 0).
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A service company receives on average 4 service requests per day. The requests are received randomly according to Poisson process. The company has 2 service engineers and sends one engineer to attend each request. 1 An engineer needs an exponentially distributed service time with the mean of day(s). 2 The company's policy is to have maximum of 2 requests waiting in the queue If this number is reached, all incoming requests are rejected (sent to a competitor). Answer the following questions based on the information provide above: (a) Using the Kendall's notation, indicate what type of queueing system it is: (b) Compute the system state probabilities (provide at least 3 decimals): Po = P1= P2 = P3 = Pa = (c) Compute the expected total number of customer requests (waiting and served) in the system. ELL] = (d) Compute the expected number of accepted requests. Aaccepted = (e) Compute the expected total processing time (waiting + being served) for the accepted requests. E[Time] =
Exercise 2. A r.v. X is Poisson distributed with parameter λ = 5.5. (a) Calculate the probability that X = 3. (b) Calculate the probability that X < 2. (c) What is the most likely (i.e. highest probability) value for X ? (You can make a graph of f(x) to find the answer).
Let X₁, X₂,...,Xn be a random sample of size, n from a normal distribution with mean, € and variance, 5. a) b) c) Find the maximum likelihood estimator for 0. Is the estimator obtained in (a) unbiased? Show that the estimator obtained in (a) is a minimum variance unbiased estimator for the parameter, 0.

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provides importan rando ables. For example, the function Poisson_pdf(x, lambda) computes the pmf at x Poisson random variable. to (a) Plot the Poisson pmf for λ = 0.5, 5, 50, as well as P[X ≤ k] and P[X > k]. A