Question

In: Statistics and Probability

In a simulated escape exercise 25 offshore oil workers participated. Normal probability plot for observations on...

In a simulated escape exercise 25 offshore oil workers participated. Normal probability plot for observations on escape time shows a linear pattern with a sample mean of 370.69 and a sample standard deviation of 24.36.

a. Calculate the 95% confidence interval for the population mean escape time

b.Calculate the interval to predict the escape time of a single worker with 95% confidence

c.Calculate the interval within which 90% of escape times can be observed with 95% confidence

d. What will happen to those intervals if we increase confidence level to 99%?

Solutions

Expert Solution

a.
TRADITIONAL METHOD
given that,
sample mean, x =370.69
standard deviation, s =24.36
sample size, n =25
I.
standard error = sd/ sqrt(n)
where,
sd = standard deviation
n = sample size
standard error = ( 24.36/ sqrt ( 25) )
= 4.872
II.
margin of error = t α/2 * (standard error)
where,
ta/2 = t-table value
level of significance, α = 0.05
from standard normal table, two tailed value of |t α/2| with n-1 = 24 d.f is 2.064
margin of error = 2.064 * 4.872
= 10.056
III.
CI = x ± margin of error
confidence interval = [ 370.69 ± 10.056 ]
= [ 360.634 , 380.746 ]
-----------------------------------------------------------------------------------------------
DIRECT METHOD
given that,
sample mean, x =370.69
standard deviation, s =24.36
sample size, n =25
level of significance, α = 0.05
from standard normal table, two tailed value of |t α/2| with n-1 = 24 d.f is 2.064
we use CI = x ± t a/2 * (sd/ Sqrt(n))
where,
x = mean
sd = standard deviation
a = 1 - (confidence level/100)
ta/2 = t-table value
CI = confidence interval
confidence interval = [ 370.69 ± t a/2 ( 24.36/ Sqrt ( 25) ]
= [ 370.69-(2.064 * 4.872) , 370.69+(2.064 * 4.872) ]
= [ 360.634 , 380.746 ]
-----------------------------------------------------------------------------------------------
interpretations:
1) we are 95% sure that the interval [ 360.634 , 380.746 ] contains the true population mean
2) If a large number of samples are collected, and a confidence interval is created
for each sample, 95% of these intervals will contains the true population mean

c.
TRADITIONAL METHOD
given that,
sample mean, x =370.69
standard deviation, s =24.36
sample size, n =25
I.
standard error = sd/ sqrt(n)
where,
sd = standard deviation
n = sample size
standard error = ( 24.36/ sqrt ( 25) )
= 4.872
II.
margin of error = t α/2 * (standard error)
where,
ta/2 = t-table value
level of significance, α = 0.1
from standard normal table, two tailed value of |t α/2| with n-1 = 24 d.f is 1.711
margin of error = 1.711 * 4.872
= 8.336
III.
CI = x ± margin of error
confidence interval = [ 370.69 ± 8.336 ]
= [ 362.354 , 379.026 ]
-----------------------------------------------------------------------------------------------
DIRECT METHOD
given that,
sample mean, x =370.69
standard deviation, s =24.36
sample size, n =25
level of significance, α = 0.1
from standard normal table, two tailed value of |t α/2| with n-1 = 24 d.f is 1.711
we use CI = x ± t a/2 * (sd/ Sqrt(n))
where,
x = mean
sd = standard deviation
a = 1 - (confidence level/100)
ta/2 = t-table value
CI = confidence interval
confidence interval = [ 370.69 ± t a/2 ( 24.36/ Sqrt ( 25) ]
= [ 370.69-(1.711 * 4.872) , 370.69+(1.711 * 4.872) ]
= [ 362.354 , 379.026 ]
-----------------------------------------------------------------------------------------------
interpretations:
1) we are 90% sure that the interval [ 362.354 , 379.026 ] contains the true population mean
2) If a large number of samples are collected, and a confidence interval is created
for each sample, 90% of these intervals will contains the true population mean

d.
TRADITIONAL METHOD
given that,
sample mean, x =370.69
standard deviation, s =24.36
sample size, n =25
I.
standard error = sd/ sqrt(n)
where,
sd = standard deviation
n = sample size
standard error = ( 24.36/ sqrt ( 25) )
= 4.872
II.
margin of error = t α/2 * (standard error)
where,
ta/2 = t-table value
level of significance, α = 0.01
from standard normal table, two tailed value of |t α/2| with n-1 = 24 d.f is 2.797
margin of error = 2.797 * 4.872
= 13.627
III.
CI = x ± margin of error
confidence interval = [ 370.69 ± 13.627 ]
= [ 357.063 , 384.317 ]
-----------------------------------------------------------------------------------------------
DIRECT METHOD
given that,
sample mean, x =370.69
standard deviation, s =24.36
sample size, n =25
level of significance, α = 0.01
from standard normal table, two tailed value of |t α/2| with n-1 = 24 d.f is 2.797
we use CI = x ± t a/2 * (sd/ Sqrt(n))
where,
x = mean
sd = standard deviation
a = 1 - (confidence level/100)
ta/2 = t-table value
CI = confidence interval
confidence interval = [ 370.69 ± t a/2 ( 24.36/ Sqrt ( 25) ]
= [ 370.69-(2.797 * 4.872) , 370.69+(2.797 * 4.872) ]
= [ 357.063 , 384.317 ]
-----------------------------------------------------------------------------------------------
interpretations:
1) we are 99% sure that the interval [ 357.063 , 384.317 ] contains the true population mean
2) If a large number of samples are collected, and a confidence interval is created
for each sample, 99% of these intervals will contains the true population mean
conclusion:
confidence interval changes from 90% to 99% then width of the interval should be increased.


Related Solutions

In a simulated escape exercise 25 offshore oil workers participated. Normal probability plot for observations on...
In a simulated escape exercise 25 offshore oil workers participated. Normal probability plot for observations on escape time shows a linear pattern with a sample mean of 370.69 and a sample standard deviation of 24.36. a. Calculate the 95% confidence interval for the population mean escape time b.Calculate the interval to predict the escape time of a single worker with 95% confidence c.Calculate the interval within which 90% of escape times can be observed with 95% confidence d. What will...
A sample of 26 offshore oil workers took part in a simulated escape exercise, resulting in...
A sample of 26 offshore oil workers took part in a simulated escape exercise, resulting in the accompanying data on time (sec) to complete the escape. 389 356 359 363 376 424 326 395 402 373 374 371 364 366 365 325 339 394 392 369 375 359 356 403 335 398 A normal probability plot of the n = 26 observations on escape time given above shows a substantial linear pattern; the sample mean and sample standard deviation are...
A sample of 26 offshore oil workers took part in a simulated escape exercise, resulting in...
A sample of 26 offshore oil workers took part in a simulated escape exercise, resulting in the accompanying data on time (sec) to complete the escape: 383 352 354 360 379 423 324 397 402 374 375 370 362 366 366 327 339 394 390 369 377 357 354 407 330 397 (a) Construct a stem-and-leaf display of the data. (Enter numbers from smallest to largest separated by spaces. Enter NONE for stems with no values.) Stems Leaves 32 1...
A sample of 22 offshore oil-workers took part in a simulated escape exercise. The sample yielded...
A sample of 22 offshore oil-workers took part in a simulated escape exercise. The sample yielded an average escape time of 388.7 min. and standard deviation of 22.4 min. The 95% confidence interval for the true average of escape time is: (379.34    ,    398.06) (378.768    ,    398.632) (380.482    ,    396.918)
A simulated exercise gave n = 22 observations on escape time (sec) for oil workers, from...
A simulated exercise gave n = 22 observations on escape time (sec) for oil workers, from which the sample mean and sample standard deviation are 370.46 and 24.89, respectively. Suppose the investigators had believed a priori that true average escape time would be at most 6 min. Does the data contradict this prior belief? Assuming normality, test the appropriate hypotheses using a significance level of 0.05. State the appropriate hypotheses. H0: μ = 360 Ha: μ < 360H0: μ =...
Construct a normal probability plot for the following sample of observations on coating thickness for low-viscosity...
Construct a normal probability plot for the following sample of observations on coating thickness for low-viscosity paint. 0.83 0.87 0.89 1.02 1.09 1.14 1.27 1.29 1.46 1.51 1.60 1.64 1.67 1.70 1.78 1.83 Determine the z percentile associated with each sample observation. (Round your answers to two decimal places.) Sample observation 0.83 0.87 0.89 1.02 1.09 1.14 1.27 1.29 z percentile Sample observation 1.46 1.51 1.60 1.64 1.67 1.70 1.78 1.83 z percentile
Construct a normal probability plot for the following sample of observations on coating thickness for low-viscosity...
Construct a normal probability plot for the following sample of observations on coating thickness for low-viscosity paint. 0.83 0.86 0.86 1.02 1.08 1.12 1.29 1.31 1.46 1.51 1.60 1.63 1.67 1.69 1.74 1.84 Determine the z percentile associated with each sample observation. (Round your answers to two decimal places.) dont just calculate , please show step by step
Construct a normal probability plot for the following sample of observations on coating thickness for low-viscosity...
Construct a normal probability plot for the following sample of observations on coating thickness for low-viscosity paint. 0.84 0.89 0.90 1.02 1.08 1.14 1.28 1.31 1.47 1.47 1.60 1.62 1.66 1.73 1.75 1.81 Determine the z percentile associated with each sample observation. (Round your answers to two decimal places.) Sample observation 0.84 0.89 0.90 1.02 1.08 1.14 1.28 1.31 z percentile Sample observation 1.47 1.47 1.60 1.62 1.66 1.73 1.75 1.81 z percentile
Exercise 10-3 A sample of 35 observations is selected from a normal population. The sample mean...
Exercise 10-3 A sample of 35 observations is selected from a normal population. The sample mean is 26, and the population standard deviation is 4. Conduct the following test of hypothesis using the .05 significance level.    H0 : μ ≤ 25 H1 : μ > 25 (a) Is this a one- or two-tailed test? "Two-tailed"-the alternate hypothesis is different from direction. "One-tailed"-the alternate hypothesis is greater than direction. (b) What is the decision rule? (Round your answer to 2...
This exercise uses the normal probability density function and requires the use of either technology or...
This exercise uses the normal probability density function and requires the use of either technology or a table of values of the standard normal distribution. The cash operating expenses of the regional phone companies during the first half of 1994 were distributed about a mean of $29.77 per access line per month, with a standard deviation of $2.55. Company A's operating expenses were $29.00 per access line per month. Assuming a normal distribution of operating expenses, estimate the percentage of...
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT