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For the conditions of Exercise 9, determine the probabilitythat the interval from \({Y_1}\;to\;{Y_n}\) will not contain thepoint 1/3.

Short Answer

Expert verified

\({\left( {\frac{1}{3}} \right)^n} + {\left( {\frac{2}{3}} \right)^n}\)

Step by step solution

01

Given information

Here,\({X_1} \ldots {X_n}\)is a random sample from a uniform distribution\(U\left( {0,1} \right)\).

\(\begin{aligned}{Y_1} &= \min \left\{ {{X_1} \ldots {X_n}} \right\}\\{Y_n} &= \max \left\{ {{X_1} \ldots {X_n}} \right\}\end{aligned}\)

02

Obtain the PDF and CDF of X

The pdf of a uniform distribution is obtained by using the formula: \(\frac{1}{{b - a}};a \le x \le b\).

Here, \(a = 0,b = 1\).

Therefore, the PDF of X is expressed as,

\({f_x} = \left\{ \begin{array}{l}\frac{1}{{1 - 0}} = 1\;\;\;\;\;\;\;\;\;\;0 \le x \le 1\\0;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;{\rm{otherwise}}\end{array} \right.\)

The CDF of a uniform distribution is obtained by using the formula:

\(\begin{aligned}{F_X}\left( x \right) &= P\left( {X \le x} \right)\\ &= \frac{{x - a}}{{b - a}}\\ &= \frac{{x - 0}}{{1 - 0}}\\ &= x\end{aligned}\)

03

Defining the probability

We have to find,

\(\begin{aligned}\\Pr \left( {{Y_1} > \frac{1}{3}\;and\;Y_n^{} < \frac{1}{3}} \right)\ &= P\left( {{Y_1} > \frac{1}{3}} \right) + P\left( {{Y_n} < \frac{1}{3}} \right)\\ &= P\left( {{X_1} > \frac{1}{3}, \ldots ,{X_n} > \frac{1}{3}} \right) + P\left( {{X_1} < \frac{1}{3}, \ldots ,{X_n} < \frac{1}{3}} \right)\\ &= {\left( {1 - F\left( {\frac{1}{3}} \right)} \right)^n} - F{\left( {\frac{1}{3}} \right)^n}\\ &= {\left( {\frac{2}{3}} \right)^n} + {\left( {\frac{1}{3}} \right)^n}\end{aligned}\)

Therefore, the answer is \({\left( {\frac{2}{3}} \right)^n} + {\left( {\frac{1}{3}} \right)^n}\)

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Most popular questions from this chapter

Suppose that the joint distribution of X and Y is uniform over a set A in the xy-plane. For which of the following sets A are X and Y independent?

a. A circle with a radius of 1 and with its center at the origin

b. A circle with a radius of 1 and with its center at the point (3,5)

c. A square with vertices at the four points (1,1), (1,โˆ’1), (โˆ’1,โˆ’1), and (โˆ’1,1)

d. A rectangle with vertices at the four points (0,0), (0,3), (1,3), and (1,0)

e. A square with vertices at the four points (0,0), (1,1),(0,2), and (โˆ’1,1)

LetXbe a random variable with a continuous distribution.

Let \({{\bf{x}}_{\bf{1}}}{\bf{ = }}{{\bf{x}}_{\bf{2}}}{\bf{ = x}}\)

a.Prove that both \({{\bf{x}}_{\bf{1}}}\) and \({{\bf{x}}_{\bf{2}}}\) have a continuous distribution.

b.Prove that \({\bf{x = (}}{{\bf{x}}_{\bf{1}}}{\bf{,}}{{\bf{x}}_{\bf{2}}}{\bf{)}}\)does not have a continuous

joint distribution.

Suppose that \({{\bf{X}}_{\bf{1}}}\;{\bf{and}}\;{{\bf{X}}_{\bf{2}}}\)are i.i.d. random variables andthat the p.d.f. of each of them is as follows:

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Find the p.d.f. of \({\bf{Y = }}{{\bf{X}}_{\bf{1}}} - {{\bf{X}}_{\bf{2}}}\)

Two students,AandB,are both registered for a certain course. Assume that studentAattends class 80 percent of the time, studentBattends class 60 percent of the time, and the absences of the two students are independent. Consider the conditions of Exercise 7 of Sec. 2.2 again. If exactly one of the two students,AandB,is in class on a given day, what is the probability that it isA?

Let X be a random vector that is split into three parts,\(X = \left( {Y,Z,W} \right)\)Suppose that X has a continuous joint distribution with p.d.f.\(f\left( {y,z,w} \right)\).Let\({g_1}\left( {y,z|w} \right)\)be the conditional p.d.f. of (Y, Z) given W = w, and let\({g_2}\left( {y|w} \right)\)be the conditional p.d.f. of Y given W = w. Prove that\({g_2}\left( {y|w} \right) = \int {{g_1}\left( {y,z|w} \right)dz} \)

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