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yarga [219]
3 years ago
13

A study of long-distance phone calls made from General Electric Corporate Headquarters in Fairfield, Connecticut, revealed the l

ength of the calls, in minutes, follows the normal probability distribution. The mean length of time per call was 4.7 minutes and the standard deviation was 0.50 minutes.
(a) What fraction of the calls last between 4.7 and 5.5 minutes? (Round z-score computation to 2 decimal places and your final answer to 4 decimal places.)Fraction of calls (b) What fraction of the calls last more than 5.5 minutes? (Round z-score computation to 2 decimal places and your final answer to 4 decimal places.)Fraction of calls (c) What fraction of the calls last between 5.5 and 6 minutes? (Round z-score computation to 2 decimal places and final answer to 4 decimal places.)Fraction of calls
(d) What fraction of the calls last between 4 and 6 minutes? (Round z-score computation to 2 decimal places and your final answer to 4 decimal places.)Fraction of calls (e) As part of her report to the president, the director of communications would like to report the length of the longest (in duration) 3% of the calls. What is this time? (Round z-score computation to 2 decimal places and your final answer to 2 decimal places.)Duration
Mathematics
1 answer:
Anna [14]3 years ago
8 0

Answer:

a) 0.4452

b) 0.0548

c) 0.0501

d) 0.9145

e) 6.08 minutes or greater

Step-by-step explanation:

We are given the following information in the question:

Mean, μ = 4.7 minutes

Standard Deviation, σ = 0.50 minutes.

We are given that the distribution of length of the calls is a bell shaped distribution that is a normal distribution.

Formula:

z_{score} = \displaystyle\frac{x-\mu}{\sigma}

a) P(calls last between 4.7 and 5.5 minutes)

P(4.7 \leq x \leq 5.5) = P(\displaystyle\frac{4.7 - 4.7}{0.50} \leq z \leq \displaystyle\frac{5.5-4.7}{0.50}) = P(0 \leq z \leq 1.6)\\\\= P(z \leq 1.6) - P(z

P(4.7 \leq x \leq 5.5) = 44.52\%

b) P(calls last more than 5.5 minutes)

P(x > 5.5) = P(z > \displaystyle\frac{5.5-4.7}{0.50}) = P(z > 1.6)\\\\P( z > 1.6) = 1 - P(z \leq 1.6)

Calculating the value from the standard normal table we have,

1 - 0.9452 = 0.0548 = 5.48\%\\P( x > 5.5) = 5.48\%

c) P( calls last between 5.5 and 6 minutes)

P(4.7 \leq x \leq 5.5) = P(\displaystyle\frac{5.5 - 4.7}{0.50} \leq z \leq \displaystyle\frac{6-4.7}{0.50}) = P(1.6 \leq z \leq 2.6)\\\\= P(z \leq 2.6) - P(z

P(5.5 \leq x \leq 6) = 5.01\%

d) P( calls last between 4 and 6 minutes)

P(4 \leq x \leq 6) = P(\displaystyle\frac{4 - 4.7}{0.50} \leq z \leq \displaystyle\frac{6-4.7}{0.50}) = P(-1.4 \leq z \leq 2.6)\\\\= P(z \leq 2.6) - P(z

P(4 \leq x \leq 6) = 91.45\%

e) We have to find the value of x such that the probability is 0.03.

P(X > x)  

P( X > x) = P( z > \displaystyle\frac{x - 4.7}{0.50})=0.03  

= 1 -P( z \leq \displaystyle\frac{x - 4.7}{0.50})=0.03  

=P( z \leq \displaystyle\frac{x - 4.7}{0.50})=0.997  

Calculation the value from standard normal z table, we have,  

P(z < 2.75) = 0.997

\displaystyle\frac{x - 4.7}{0.50} = 2.75\\x = 6.075 \approx 6.08  

Hence, the call lengths must be 6.08 minutes or greater for them to lie in the highest 3%.

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The 99.9% confidence interval for the difference between these results is between 12.09 and 25.91 hours per week.

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Before building the confidence interval, we need to understand the central limit theorem and subtraction between normal variables.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean \mu and standard deviation \sigma, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}.

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean \mu = p and standard deviation s = \sqrt{\frac{p(1-p)}{n}}

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When two normal variables are subtracted, the mean is the subtraction of the means, while the standard deviation is the square root of the sum of variances.

Ohio had an average of 52, standard deviation of 7.22, sample of 40.

This means that \mu_O = 52, s_O = \frac{7.22}{\sqrt{40}} = 1.1416

Pennsylvania had an average of 33, standard deviation of 10.11, sample of 33.

This means that \mu_P = 33, s_P = \frac{10.11}{\sqrt{33}} = 1.76

Distribution of the difference:

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We have that to find our \alpha level, that is the subtraction of 1 by the confidence interval divided by 2. So:

\alpha = \frac{1 - 0.999}{2} = 0.0005

Now, we have to find z in the Ztable as such z has a pvalue of 1 - \alpha.

That is z with a pvalue of 1 - 0.0005 = 0.9995, so Z = 3.29.

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The lower end of the interval is the sample mean subtracted by M. So it is 19 - 6.91 = 12.09 hours per week

The upper end of the interval is the sample mean added to M. So it is 19 + 6.91 = 25.91 hours per week.

The 99.9% confidence interval for the difference between these results is between 12.09 and 25.91 hours per week.

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