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adelina 88 [10]
1 year ago
8

Each airline passenger and his or her luggage must be checked to determine whether he or she is carrying weapons on to the airpl

ane. Suppose that at O’hare International Airport, an average of 10 passengers per minute arrive (interarrival times are exponential). To check passengers for weapons, the airport must have a checkpoint consisting of a metal detector and baggage X-ray machine. Whenever a checkpoint is in operation, two employees are required. A checkpoint can check an average of 12 passengers per minute (the time to check a passenger is exponential). Under the assumption that the airport has only one checkpoint, answer the following questions:
a. What is the probability that a passenger will have to wait before being checked for weapons?
b. On the average, how many passengers are waiting in line to enter the checkpoint?
c. On the average, how long will a passenger spend at the checkpoint?
Mathematics
1 answer:
Dafna1 [17]1 year ago
5 0

Answer:

a

P_k  = 0.83

b

 N_{\mu} \approx  4 \ passengers

c

T_{\lambda} =  0.5 \ minutes

Step-by-step explanation:

From the question we are told that

The average number of passengers that arrive per minute is \lambda = 10

The average number of check that can be carried out in one minute is \mu= 12

Generally the probability that a passenger will have to wait before being checked for weapons is mathematically represented as

        P_k  = \frac{\lambda }{ \mu }

=>    P_k  = \frac{10 }{ 12}

=>    P_k  = 0.83

Generally the number of passengers are waiting in line to enter the checkpoint is mathematically represented as

     N_{\mu} =  \frac{\lambda^2}{\mu (\mu -\lambda) }

=>  N_{\mu} =  \frac{10^2}{12 (12 -10) }

=>  N_{\mu} \approx  4 \ passengers

Generally the average time a passenger spend at the checkpoint is mathematically represented as

      T_{\lambda} = \frac{ \frac{\lambda}{(\mu - \lambda)} }{ \lambda}

=>   T_{\lambda} = \frac{ \frac{ 10}{(12 - 10)} }{10}

=>   T_{\lambda} =  0.5 \ minutes

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(1) We are conducting the one-sample t-test and will be testing for the hypothesis of mean battery greater than 25 h. So, we will reject the null hypothesis for mean battery life equal to 25 hr against the alternative hypothesis for mean battery life greater than 25 hr.

(2) A 95% lower confidence interval on mean battery life is 25.06 hr.

Step-by-step explanation:

We are given that a random sample of 10 batteries is subjected to an accelerated life test by running them continuously at an elevated temperature until failure, and the following lifetimes (in hours) are obtained: 25.5, 26.1, 26.8, 23.2, 24.2, 28.4, 25.0, 27.8, 27.3, and 25.7.

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                              P.Q.  =  \frac{\bar X-\mu}{\frac{s}{\sqrt{n} } }  ~  t_n_-_1

where, \bar X = sample mean BAC = \frac{\sum X}{n} = 26 hr

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             n = sample of batteries = 10

             \mu = population mean battery life

<em> Here for constructing a 95% lower confidence interval we have used a One-sample t-test statistics because we don't know about population standard deviation. </em>

(1) It is stated that the manufacturer wants to be certain that the mean battery life exceeds 25 h.

Since we are conducting the one-sample t-test and will be testing for the hypothesis of mean battery greater than 25 h. So, we will reject the null hypothesis for mean battery life equal to 25 hr against the alternative hypothesis for mean battery life greater than 25 hr.

(2) So, a 95% lower confidence interval for the population mean, \mu is;

P(-1.833 < t_9) = 0.95  {As the lower critical value of t at 9

                                             degrees of  freedom is -1.833 with P = 5%}    

P(-1.833 < \frac{\bar X-\mu}{\frac{s}{\sqrt{n} } }) = 0.95

P( -1.833 \times {\frac{s}{\sqrt{n} } } < {\bar X-\mu} ) = 0.95

P( \bar X-1.833 \times {\frac{s}{\sqrt{n} } } < \mu) = 0.95

<u>95% lower confidence interval for</u> \mu = [ \bar X-1.833 \times {\frac{s}{\sqrt{n} } } ]

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Therefore, a 95% lower confidence interval on mean battery life is 25.06 hr.

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