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Suppose that the number of minutes required to serve a customer at the checkout counter of a supermarket has an exponential distribution for which the mean is 3. Using the central limit theorem, approximate the probability that the total time required to serve a random sample of 16 customers will exceed one hour.

Short Answer

Expert verified

Probability of the time required to serve a random sample of 16 customer exceed one hour is 0.1587

Step by step solution

01

Given information

Number of minutes required to serve a customer at checkout counter of a supermarket follows exponential distribution with mean 3.

02

Calculate probability of the total time required to serve a random sample of 16 customers 

Let \({X_1}...{X_{16}}\) be the time required to serve the 16 customers.

The parameter of the exponential distribution is\(\frac{1}{3}\)

According to theorem 5.7.8,the mean of each\({X_i}\)are 3 and 9 respectively.

Let\(\sum\limits_{k = 1}^{16} {{X_k} = Y} \) be the total time.

The central limit theorem approximation to the distribution of Y is the normal distribution with mean

\(16 \times 3 = 48\)

The variance\(16 \times 9 = 144\).

The approximate probability that\(Y > 60\)is

\(\begin{align}1 - \phi \left( {\frac{{60 - 48}}{{\sqrt {144} }}} \right) &= 1 - \phi \left( {\frac{{12}}{{\sqrt {144} }}} \right)\\ &= 1 - \phi \left( 1 \right)\\ &= 0.1587\end{align}\)

Hence Probability of the time required to serve a random sample of 16 customer exceed one hour is 0.1587

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Most popular questions from this chapter

Using the correction for continuity, determine the probability required in Exercise 7 of Sec. 6.3.

Suppose that X is a random variable such that\(E\left( {{X^k}} \right)\)exists and\({\rm P}\left( {X \ge 0} \right) = 1\). Prove that for\(k > 0\)and\(t > 0\),\({\rm P}\left( {X \ge t} \right) \le \frac{{E\left( {{X^k}} \right)}}{{{t^k}}}\)

Prove theorem 6.2.7

Suppose that X is a random variable for which E(X) = μ and \({\bf{E}}\left[ {{{\left( {{\bf{X - \mu }}} \right)}^{\bf{4}}}} \right]{\bf{ = }}{{\bf{\beta }}^{\bf{4}}}\) Prove that

\({\bf{P}}\left( {\left| {{\bf{X - \mu }}} \right| \ge {\bf{t}}} \right) \le \frac{{{{\bf{\beta }}_{\bf{4}}}}}{{{{\bf{t}}^{\bf{4}}}}}\)

In this exercise, we construct an example of a sequence of random variables \({Z_n}\) such that but \(\Pr \left( {\mathop {\lim }\limits_{n \to \infty } \;{Z_n} = 0} \right) = 0\).That is, \({Z_n}\)converges in probability to 0, but \({Z_n}\)does not converge to 0 with probability 1. Indeed, \({Z_n}\)converges to 0 with probability 0.

Let Xbe a random variable having the uniform distribution on the interval\(\left[ {0,1} \right]\). We will construct a sequence of functions \({h_n}\left( x \right)\;for\;n = 1,2,...\)and define\({Z_n} = {h_n}\left( X \right)\). Each function \({h_n}\) will take only two values, 0 and 1. The set of x where \({h_n}\left( x \right) = 1\) is determined by dividing the interval \(\left[ {0,1} \right]\)into k non-overlapping subintervals of length\(\frac{1}{k}\;for\;k = 1,2,...\)arranging these intervals in sequence, and letting \({h_n}\left( x \right) = 1\) on the nth interval in the sequence for \(n = 1,2,...\)For each k, there are k non overlapping subintervals, so the number of subintervals with lengths \(1,\frac{1}{2},\frac{1}{3},...,\frac{1}{k}\) is \(1 + 2 + 3 + ... + k = \frac{{k\left( {k + 1} \right)}}{2}\)

The remainder of the construction is based on this formula. The first interval in the sequence has length 1, the next two have length ½, the next three have length 1/3, etc.

  1. For each\(n = 1,2,...\), proved that there is a unique positive integer \({k_n}\) such that \(\frac{{\left( {{k_n} - 1} \right){k_n}}}{2} < n \le \frac{{{k_n}\left( {{k_n} + 1} \right)}}{2}\)
  2. For each\(n = 1,2,..\;\), let\({j_n} = n - \frac{{\left( {{k_n} - 1} \right){k_n}}}{2}\). Show that \({j_n}\) takes the values \(1,...,{k_n}\;\)as n runs through\(1 + \frac{{\left( {{k_n} - 1} \right){k_n}}}{2},...,\frac{{{k_n}\left( {{k_n} + 1} \right)}}{2}\).
  3. Define \({h_n}\left( x \right) = \left\{ \begin{array}{l}1\;if\;\frac{{\left( {{j_n} - 1} \right)}}{{{k_n}}} \le x < \frac{{{j_n}}}{{{k_n}}},\\0\;if\;not\end{array} \right.\)

Show that, for every \(x \in \left[ {0,1} \right),\;{h_n}\left( x \right) = 1\) for one and only one n among \(1 + \frac{{\left( {{k_n} - 1} \right){k_n}}}{2},...,\frac{{{k_n}\left( {{k_n} + 1} \right)}}{2}\)

  1. Show that \({Z_n} = {h_n}\left( X \right)\)takes the value 1 infinitely often with probability 1 \(\)
  1. Show that(6.2.18) holds.
  1. Show that\(\Pr \left( {{Z_n} = 0} \right) = 1 - \frac{1}{{{K_n}}}\)and \(\mathop {\lim }\limits_{n \to \infty } \;{k_n} = \infty \;\).
  2. Show that
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