6. Consider the cdf of a random variable X defined below.0,I <00 ≤ x < 11 1.5)P(X ≤ 0.25)P(X < 6.75)P(X > -10)2345X

Given: C.D.F of a random variable X FX (x) = 0 x < 014x…

Answered: 6. Consider the cdf of a random…

A First Course in Probability (10th Edition)

10th Edition

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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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O Three uncorrelated random variables X1, X, andX3 have means 1, - 3 and 1.5 and second moments 2.5, 11 and 3.5 respectively. Let Y = X1- 2X2+ 3X3 be a new random variable. Find (a) mean value (b) variance of Y.
Suppose that X, Y, and Z are jointly distributed random variables, that is, they are defined on the same sample space. Suppose that we also have the following. E(X)=-1 E(Y) = 4 E (Z)=0 Var(X)=20 Var(Y) = 27 Var (Z) = 38 Compute the values of the expressions below. E (5Z-2) = 0 2Z+X -5 Var(2X-3) = 0 E = E (32²) = 0 Explanation Check X O hp Ⓒ2022 McGraw Hill LLC. All Rights Reserved. Term
23. Let Y denote a geometric random variable with probability of success p. (a) Show that for a positive integer a, P(Y > a) = (1 − p)a (b) Showthatforapositiveintegeraandb,P(Y >a+b|Y >a)=(1−p)a =P(Y >b). (c) Why do you think the property in (b) is called the memoryless property of the geometric distribution? (d) In the development of the distribution of the geometric random variable, we assumed that the experiment consisted of conducting identical and independent trials until the first success was observed. In light of these assumptions, why is the result in part (b) “obvious”? (e) Show that P (Y = an odd integer) = p 1−q2 24. Given that the random variable W is binomial distribution with n trials and success probability p in each trial and P (W = w) = h(w), show that (a) h(w) =p(n−w+1),w>0 h(w − 1) (1 − p)w (b) E [W(W − 1)]= n(n − 1)p2. ? 1 ? 1−(1−p)n+1 (c)E W+1 = (n+1)p
5. Let X and Y be independent random variables with each following N(0,1). Consider the random variable W defined by { if XY > 0, if XY 0 and W < 0. Show work.
2. Some properties of Expected value and variance of a random variable. a) Assume that X is an arbitrary discrete random variable, and a and b are constant. Using the definitic Show: and E(aX + b) = a · E(X) + b V(aX + b) = a² · V(X ) Stat 3128 Ali Mahzarnia P STAT 3128 Ali Mahzarnia b) Justify the computational formula of Variance of a random variable which is to justify : V(X) = E[(X – µ°] = Ex – µ)P • ptx) = | 2: Here needs justification By Cauchy Schwarz inequality it can be shown that the right hand side is always positive. Analogs expression in Mechanic : Parallel axis theorem Iem = I– md² Moment of Moment of inetria about Inertia of an an axis shifted object about the center of a mass by d from center of mass (a parallel shift) Icm is dispersion around the mean and is like second central moment (variance) I is like second moment if d is mean m is like sum total all the weight of each of the x which all add up to 1 d squared is like squared of mean since we,
Let x be a poisson random variable with mean = 6.5. Find the probabilities of x using the poisson formula. A) P(x=0) B) P(x=1) C) P(x=2) D) P(x
The possible values of a discrete random variable X are 0, 1, 3, and 6 with respective probabilities 0.2, 0.3, 0.1, 0.4. Find E[X] and Var(X).
15. Let X be a discrete random variable with the following cdf: x < -2 2≤ x < -1 - 1
A discrete random variable x has the the following probability distribution X 2 3 4 5 6 P(X=x) 3k2k2 3k2k2 3kk 2kk kk i) Find the value of k? ii) Find P[X>5][3.5<X<6.5]P[X>5][3.5<X<6.5]
Example 5-49. Let the p.d.f. of the random variable (x, y) be : a-² e-(x+y)/a ; x, y > 0, a > 0 f(x, y) = { , elsewhere Find the distribution of (X-Y).
1. Provide the graphs for PDF and CDF of the random variable B which takes on values of 1 and 0 with probability of 0.40 and 0.60. (Don't forget the horizontal axis must be sorted).
Suppose that X, Y, and Z are jointly distributed random variables, that is, they are defined on the same sample space. Suppose that we also have the following. E(Y) = 4 Var(X) = 24 Var(Y) 18 Var(z)=7 E(X)= -3 E(Z) =-1 Compute the values of the expressions below. E(-4-5X)= 0 %3D ? ()- 0 Var(-3+2.X) = 0

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