Applied Probability and Statistics Chapter 8 Solutions...

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Willie Seo

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September 22, 2010

CHAPTER 8 Section 8-1 8-1

a) The confidence level for

x − 2.14σ / n ≤ µ ≤ x + 2.14σ / n is determined by the

value of z0 which is 2.14. From Table III, Φ(2.14) = P(Z<2.14) = 0.9838 and the confidence level is 2(0.9838-0.5) = 96.76%. b) The confidence level for

x − 2.49σ / n ≤ µ ≤ x + 2.49σ / n is determined by the

by the value of z0 which is 2.14. From Table III, Φ(2.49) = P(Z<2.49) = 0.9936 and the confidence level is is 2(0.9936-0.5) = 98.72%. c) The confidence level for

x − 1.85σ / n ≤ µ ≤ x + 1.85σ / n is determined by the

by the value of z0 which is 2.14. From Table III, Φ(1.85) = P(Z<1.85) = 0.9678 and the confidence level is 93.56%. 8-2

a) A z = 2.33 would give result in a 98% two-sided confidence interval. b) A z = 1.29 would give result in a 80% two-sided confidence interval. c) A z = 1.15 would give result in a 75% two-sided confidence interval. α

α

α

8-3

a) A z = 1.29 would give result in a 90% one-sided confidence interval. b) A z = 1.65 would give result in a 95% one-sided confidence interval. c) A z = 2.33 would give result in a 99% one-sided confidence interval. α

α

α

8-4

a) 95% CI for

µ,

n = 10, σ = 20 x = 1000, z = 1.96

x − zσ / n ≤ µ ≤ x + zσ / n 1000 − 1.96(20 / 10 ) ≤ µ ≤ 1000 + 1.96(20 / 10 ) 987.6 ≤ µ ≤ 1012.4 b) .95% CI for µ , n = 25, σ = 20 x = 1000, z = 1.96 x − zσ / n ≤ µ ≤ x + zσ / n 1000 − 1.96(20 / 25 ) ≤ µ ≤ 1000 + 1.96(20 / 25 ) 992.2 ≤ µ ≤ 1007.8 c) 99% CI for µ , n = 10, σ = 20 x = 1000, z = 2.58 x − zσ / n ≤ µ ≤ x + zσ / n 1000 − 2.58(20 / 10 ) ≤ µ ≤ 1000 + 2.58(20 / 10 ) 983.7 ≤ µ ≤ 1016.3 d) 99% CI for µ , n = 25, σ = 20 x = 1000, z = 2.58 x − zσ / n ≤ µ ≤ x + zσ / n 1000 − 2.58(20 / 25 ) ≤ µ ≤ 1000 + 2.58(20 / 25 ) 989.7 ≤ µ ≤ 1010.3 e) When n is larger, the CI is narrower. The higher the confidence level, the wider the CI.

8-1

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

8-5

a) Sample mean from the first confidence interval = 38.02 + (61.98-38.02)/2 = 50 Sample mean from the second confidence interval = 39.95 + (60.05-39.95)/2 = 50 b) The 95% CI is (38.02, 61.98) and the 90% CI is (39.95, 60.05). The higher the confidence level, the wider the CI.

8-6

a) Sample mean from the first confidence interval =37.53 + (49.87-37.53)/2 = 43.7 Sample mean from the second confidence interval =35.59 + (51.81-35.59)/2 = 43.7 b) The 99% CI is (35.59, 51.81) and the 95% CI is (37.53, 49.87). The higher the confidence level, the wider the CI.

8-7

a) Find n for the length of the 95% CI to be 40. Za/2 = 1.96 1/2 length = (1.96)(20) / n = 20

39.2 = 20 n 2

⎛ 39.2 ⎞ n = ⎜ ⎟ = 3.84 ⎝ 20 ⎠ Therefore, n = 4. b) Find n for the length of the 99% CI to be 40. Za/2 = 2.58 1/2 length = (2.58)(20) / n = 20 51.6 = 20 n 2

⎛ 51.6 ⎞ n = ⎜ ⎟ = 6.66 ⎝ 20 ⎠ Therefore, n = 7.

8-8

Interval (1): 3124.9 ≤ µ ≤ 3215.7 and Interval (2): 3110.5 ≤ µ ≤ 3230.1 Interval (1): half-length =90.8/2=45.4 and Interval (2): half-length =119.6/2=59.8

x1 = 3124.9 + 45.4 = 3170.3 x2 = 3110.5 + 59.8 = 3170.3 The sample means are the same.

a)

b) Interval (1): 3124.9 ≤ µ ≤ 3215.7 was calculated with 95% Confidence because it has a smaller half-length, and therefore a smaller confidence interval. The 99% confidence level will make the interval larger. 8-9

a) The 99% CI on the mean calcium concentration would be longer. b) No, that is not the correct interpretation of a confidence interval. The probability that µ is between 0.49 and 0.82 is either 0 or 1. c) Yes, this is the correct interpretation of a confidence interval. The upper and lower limits of the confidence limits are random variables.

8-10

95% Two-sided CI on the breaking strength of yarn: where x = 98, σ = 2 , n=9 and z0.025 = 1.96 x − z 0.025σ / n ≤ µ ≤ x + z 0.025σ / n 98 − 1.96(2) / 9 ≤ µ ≤ 98 + 1.96(2) / 9 96.7 ≤ µ ≤ 99.3

8-11

95% Two-sided CI on the true mean yield: where x = 90.480, σ = 3 , n=5 and z0.025 = 1.96 x − z 0.025σ / n ≤ µ ≤ x + z 0.025σ / n 90.480 − 1.96(3) / 5 ≤ µ ≤ 90.480 + 1.96(3) / 5 87.85 ≤ µ ≤ 93.11

8-2

Applied Statistics and Probability for Engineers, 5th edition

8-12

September 22, 2010

99% Two-sided CI on the diameter cable harness holes: where x =1.5045 , σ = 0.01 , n=10 and z0.005 = 2.58 x − z 0.005σ / n ≤ µ ≤ x + z 0.005σ / n 1.5045 − 2.58(0.01) / 10 ≤ µ ≤ 1.5045 + 2.58(0.01) / 10 1.4963 ≤ µ ≤ 1.5127

8-13

a) 99% Two-sided CI on the true mean piston ring diameter For α = 0.01, z /2 = z0.005 = 2.58 , and x = 74.036, σ = 0.001, n=15 ⎛ σ ⎞ ⎛ σ ⎞ x − z0.005 ⎜ ⎟ ≤ µ ≤ x + z0.005 ⎜ ⎟ ⎝ n ⎠ ⎝ n ⎠ α

⎛ 0.001⎞ ⎛ 0.001⎞ 74.036 − 2.58⎜ ⎟ ≤ µ ≤ 74.036 + 2.58⎜ ⎟ ⎝ 15 ⎠ ⎝ 15 ⎠

74.0353 ≤ µ ≤ 74.0367 b) 99% One-sided CI on the true mean piston ring diameter For α = 0.01, z = z0.01 =2.33 and x = 74.036, σ = 0.001, n=15 α

x − z 0.01

σ n

≤µ

⎛ 0.001 ⎞ ⎟⎟ ≤ µ 74.036 − 2.33⎜⎜ ⎝ 15 ⎠ 74.0354≤ µ The lower bound of the one sided confidence interval is less than the lower bound of the two-sided confidence. This is because the Type I probability of 99% one sided confidence interval (or α = 0.01) in the left tail (or in the lower bound) is greater than Type I probability of 99% two-sided confidence interval (or α/2 = 0.005) in the left tail. 8-14

a) 95% Two-sided CI on the true mean life of a 75-watt light bulb For α = 0.05, z /2 = z0.025 = 1.96 , and x = 1014, σ =25 , n=20 α

⎛ σ ⎞ ⎛ σ ⎞ x − z 0.025 ⎜⎜ ⎟⎟ ≤ µ ≤ x + z 0.025 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 25 ⎞ ⎛ 25 ⎞ 1014 − 1.96⎜⎜ ⎟⎟ ≤ µ ≤ 1014 + 1.96⎜⎜ ⎟⎟ ⎝ 20 ⎠ ⎝ 20 ⎠ 1003 ≤ µ ≤ 1025 b) 95% One-sided CI on the true mean piston ring diameter For α = 0.05, z = z0.05 =1.65 and x = 1014, σ =25 , n=20 α

x − z0.05

σ n

≤µ

⎛ 25 ⎞ 1014 − 1.65⎜ ⎟ ≤ µ ⎝ 20 ⎠ 1005 ≤ µ

8-3

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

The lower bound of the one sided confidence interval is lower than the lower bound of the two-sided confidence interval even though the level of significance is the same. This is because all of the Type I probability (or α) is in the left tail (or in the lower bound).

8-15

a) 95% two sided CI on the mean compressive strength z /2 = z0.025 = 1.96, and x = 3250, σ2 = 1000, n=12 ⎛ σ ⎞ ⎛ σ ⎞ x − z0.025 ⎜ ⎟ ≤ µ ≤ x + z0.025 ⎜ ⎟ ⎝ n ⎠ ⎝ n ⎠ α

⎛ 31.62 ⎞ ⎛ 31.62 ⎞ 3250 − 1.96⎜ ⎟ ≤ µ ≤ 3250 + 1.96⎜ ⎟ ⎝ 12 ⎠ ⎝ 12 ⎠ 3232.11 ≤ µ ≤ 3267.89 b) 99% Two-sided CI on the true mean compressive strength z /2 = z0.005 = 2.58 α

⎛ σ ⎞ ⎛ σ ⎞ x − z0.005 ⎜ ⎟ ≤ µ ≤ x + z0.005 ⎜ ⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 31.62 ⎞ ⎛ 31.62 ⎞ 3250 − 2.58⎜ ⎟ ≤ µ ≤ 3250 + 2.58⎜ ⎟ ⎝ 12 ⎠ ⎝ 12 ⎠ 3226.4 ≤ µ ≤ 3273.6 The 99% CI is wider than the 95% CI 8-16

95% Confident that the error of estimating the true mean life of a 75-watt light bulb is less than 5 hours. For α = 0.05, z /2 = z0.025 = 1.96 , and σ =25 , E=5 α

2

2

⎛ z σ ⎞ ⎛ 1.96(25) ⎞ n = ⎜ a / 2 ⎟ = ⎜ ⎟ = 96.04 5 ⎝ ⎠ ⎝ E ⎠

Always round up to the next number, therefore n = 97 8-17

Set the width to 6 hours with σ = 25, z0.025 = 1.96 solve for n. 1/2 width = (1.96)(25) / n = 3

49 = 3 n 2

⎛ 49 ⎞ n = ⎜ ⎟ = 266.78 ⎝ 3 ⎠ Therefore, n = 267. 8-18

99% Confident that the error of estimating the true compressive strength is less than 15 psi For α = 0.01, z /2 = z0.005 = 2.58 , and σ =31.62 , E=15 α

2

2

⎛ z σ ⎞ ⎛ 2.58(31.62) ⎞ n = ⎜ a / 2 ⎟ = ⎜ ⎟ = 29.6 ≅ 30 15 ⎝ ⎠ ⎝ E ⎠

8-4

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

Therefore, n=30 8-19

To decrease the length of the CI by one half, the sample size must be increased by 4 times (22).

zα / 2σ / n = 0.5l Now, to decrease by half, divide both sides by 2. ( zα / 2σ / n ) / 2 = (l / 2) / 2

( zα / 2σ / 2 n ) = l / 4 ( zα / 2σ / 22 n ) = l / 4 Therefore, the sample size must be increased by 22. 8-20

⎛ σ ⎞ ⎛ σ ⎞ x − zα / 2 ⎜⎜ ⎟⎟ ≤ µ ≤ x + zα / 2 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ zα / 2σ z σ z σ 1 ⎛ zα / 2σ ⎞ ⎜ ⎟ = α/2 = α/2 = 2n 1.414 n 1.414 n 1.414 ⎜⎝ n ⎟⎠

If n is doubled in Eq 8-7:

The interval is reduced by 0.293 29.3%

If n is increased by a factor of 4 Eq 8-7:

zα / 2σ 4n

=

zα / 2σ

=

2 n

zα / 2σ

1 ⎛ z σ = ⎜⎜ α / 2 2 ⎝ n 2 n

The interval is reduced by 0.5 or ½. 8-21

⎞ ⎟⎟ ⎠

a) 99% two sided CI on the mean temperature z /2 = z0.005 = 2.57, and x = 13.77, σ = 0.5, n=11 α

⎛ σ ⎞ ⎛ σ ⎞ x − z 0.005 ⎜⎜ ⎟⎟ ≤ µ ≤ x + z 0.005 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 0.5 ⎞ ⎛ 0.5 ⎞ 13.77 − 2.57⎜ ⎟ ≤ µ ≤ 13.77 + 2.57⎜ ⎟ ⎝ 11 ⎠ ⎝ 11 ⎠ 13.383 ≤ µ ≤ 14.157 b) 95% lower-confidence bound on the mean temperature For α = 0.05, z = z0.05 =1.65 and x = 13.77, σ = 0.5, n =11 α

x − z 0.05

σ n

≤µ

⎛ 0.5 ⎞ ⎟⎟ ≤ µ 13.77 − 1.65⎜⎜ ⎝ 11 ⎠ 13.521 ≤ µ c) 95% confidence that the error of estimating the mean temperature for wheat grown is less than 2 degrees Celsius. For α = 0.05, z /2 = z0.025 = 1.96, and σ = 0.5, E = 2 α

2

2

⎛ z σ ⎞ ⎛ 1.96(0.5) ⎞ n = ⎜ a / 2 ⎟ = ⎜ ⎟ = 0.2401 2 ⎝ ⎠ ⎝ E ⎠

Always round up to the next number, therefore n = 1.

8-5

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

d) Set the width to 1.5 degrees Celsius with σ = 0.5, z0.025 = 1.96 solve for n. 1/2 width = (1.96)(0.5) / n = 0.75

0.98 = 0.75 n 2

⎛ 0.98 ⎞ n = ⎜ ⎟ = 1.707 ⎝ 0.75 ⎠ Therefore, n = 2. Section 8-2 8-22

t 0.025,15 = 2.131

t 0.05,10 = 1.812

t 0.005, 25 = 2.787

t 0.001,30 = 3.385

a)

t 0.025,12 = 2.179

d)

t 0.0005 ,15 = 4.073

8-24

a)

t 0.05,14 = 1.761

8-25

a) Mean = sum = 251.848 = 25.1848

8-23

N

t 0.10, 20 = 1.325

b)

t 0.025, 24 = 2.064

c)

t 0.005,13 = 3.012

b)

t 0.01,19 = 2.539

c)

t 0.001, 24 = 3.467

10

Variance = = ( stDev) 2 = 1.6052 = 2.5760 b) 95% confidence interval on mean

n = 10 x = 25.1848 s = 1.605 t0.025,9 = 2.262 ⎛ s ⎞ ⎛ s ⎞ x − t 0.025,9 ⎜ ⎟ ≤ µ ≤ x + t 0.025,9 ⎜ ⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 1.605 ⎞ ⎛ 1.605 ⎞ 25.1848 − 2.262⎜ ⎟ ≤ µ ≤ 25.1848 + 2.262⎜ ⎟ ⎝ 10 ⎠ ⎝ 10 ⎠ 24.037 ≤ µ ≤ 26.333 8-26

SE Mean =

stDev

6.11

= 1.58 , therefore N = 15 N N sum 751.40 Mean = = = 50.0933 N 15 Variance = ( stDev) 2 = 6.112 = 37.3321 =

b) 95% confidence interval on mean

n = 15 x = 50.0933 s = 6.11 t0.025,14 = 2.145 ⎛ s ⎞ ⎛ s ⎞ x − t 0.025,14 ⎜ ⎟ ≤ µ ≤ x + t 0.025,14 ⎜ ⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 6.11 ⎞ ⎛ 6.11 ⎞ 50.0933 − 2.145⎜ ⎟ ≤ µ ≤ 50.0933 + 2.145⎜ ⎟ ⎝ 15 ⎠ ⎝ 15 ⎠ 46.709 ≤ µ ≤ 53.477

8-6

Applied Statistics and Probability for Engineers, 5th edition

8-27

95% confidence interval on mean tire life

n = 16 x = 60,139.7 s = 3645.94 t 0.025,15 = 2.131 ⎛ s ⎞ ⎛ s ⎞ x − t 0.025,15 ⎜⎜ ⎟⎟ ≤ µ ≤ x + t 0.025,15 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 3645.94 ⎞ ⎛ 3645.94 ⎞ 60139.7 − 2.131⎜⎜ ⎟⎟ ≤ µ ≤ 60139.7 + 2.131⎜⎜ ⎟⎟ 16 ⎠ 16 ⎠ ⎝ ⎝ 58197.33 ≤ µ ≤ 62082.07 8-28

99% lower confidence bound on mean Izod impact strength

n = 20 x = 1.25 s = 0.25 t 0.01,19 = 2.539 ⎛ s ⎞ x − t 0.01,19 ⎜⎜ ⎟⎟ ≤ µ ⎝ n ⎠ ⎛ 0.25 ⎞ 1.25 − 2.539⎜⎜ ⎟⎟ ≤ µ ⎝ 20 ⎠ 1.108 ≤ µ 8-29

x = 1.10 s = 0.015 n = 25 95% CI on the mean volume of syrup dispensed For α = 0.05 and n = 25, t /2,n-1 = t0.025,24 = 2.064 α

⎛ s ⎞ ⎛ s ⎞ x − t 0.025, 24 ⎜⎜ ⎟⎟ ≤ µ ≤ x + t 0.025, 24 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 0.015 ⎞ ⎛ 0.015 ⎞ 1.10 − 2.064⎜⎜ ⎟⎟ ≤ µ ≤ 1.10 + 2.064⎜⎜ ⎟⎟ ⎝ 25 ⎠ ⎝ 25 ⎠ 1.094 ≤ µ ≤ 1.106 8-30

95% confidence interval on mean peak power

n = 7 x = 315 s = 16 t 0.025, 6 = 2.447 ⎛ s ⎞ ⎛ s ⎞ x − t 0.025, 6 ⎜⎜ ⎟⎟ ≤ µ ≤ x + t 0.025, 6 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 16 ⎞ ⎛ 315 ⎞ 315 − 2.447⎜⎜ ⎟⎟ ≤ µ ≤ 315 + 2.447⎜⎜ ⎟⎟ ⎝ 7 ⎠ ⎝ 7 ⎠ 300.202 ≤ µ ≤ 329.798 8-31

99% upper confidence interval on mean SBP

n = 14 x = 118.3 s = 9.9 t 0.01,13 = 2.650

8-7

September 22, 2010

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

⎛ s ⎞ ⎟⎟ ⎝ n ⎠ ⎛ 9.9 ⎞ µ ≤ 118.3 + 2.650⎜ ⎟ ⎝ 14 ⎠ µ ≤ 125.312

µ ≤ x + t 0.005,13 ⎜⎜

8-32

90% CI on the mean frequency of a beam subjected to loads

x = 231.67, s = 1.53, n = 5, tα/2,n -1 = t.05, 4 = 2.132 ⎛ s ⎞ ⎛ s ⎞ x − t 0.05, 4 ⎜⎜ ⎟⎟ ≤ µ ≤ x + t 0.05, 4 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 1.53 ⎞ ⎛ 1.53 ⎞ 231.67 − 2.132⎜⎜ ⎟⎟ ≤ µ ≤ 231.67 − 2.132⎜⎜ ⎟⎟ ⎝ 5 ⎠ ⎝ 5 ⎠ 230.2 ≤ µ ≤ 233.1 By examining the normal probability plot, it appears that the data are normally distributed. There does not appear to be enough evidence to reject the hypothesis that the frequencies are normally distributed. The data appear to be normally distributed based on examination of the normal probability plot below. Normal Probability Plot for frequencies ML Estimates - 95% CI

99

ML Estimates

95 90

Mean

231.67

StDev

1.36944

80

Percent

8-33

70 60 50 40 30 20 10 5 1 226

231

236

Data

Therefore, there is evidence to support that the annual rainfall is normally distributed.

8-8

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

Probability Plot of Rainfall Normal - 95% CI

99

Mean StDev N AD P-Value

95 90

485.9 90.30 20 0.288 0.581

Percent

80 70 60 50 40 30 20 10 5

1

200

300

400

500 Rainfall

600

700

800

95% confidence interval on mean annual rainfall

n = 20 x = 485.8 s = 90.34 t 0.025,19 = 2.093 ⎛ s ⎞ ⎛ s ⎞ x − t 0.025,19 ⎜⎜ ⎟⎟ ≤ µ ≤ x + t 0.025,19 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 90.34 ⎞ ⎛ 90.34 ⎞ 485.8 − 2.093⎜⎜ ⎟⎟ ≤ µ ≤ 485.8 + 2.093⎜⎜ ⎟⎟ ⎝ 20 ⎠ ⎝ 20 ⎠ 443.520 ≤ µ ≤ 528.080 The data appear to be normally distributed based on examination of the normal probability plot below. Therefore, there is evidence to support that the solar energy is normally distributed. Probability Plot of Solar Normal - 95% CI

99

Mean StDev N AD P-Value

95 90 80

Percent

8-34

70 60 50 40 30 20 10 5

1

50

55

60

65 Solar

70

95% confidence interval on mean solar energy consumed

n = 16 x = 65.58 s = 4.225 t 0.025,15 = 2.131

8-9

75

80

65.58 4.225 16 0.386 0.349

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

⎛ s ⎞ ⎛ s ⎞ x − t 0.025,15 ⎜⎜ ⎟⎟ ≤ µ ≤ x + t 0.025,15 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 4.225 ⎞ ⎛ 4.225 ⎞ 65.58 − 2.131⎜⎜ ⎟⎟ ≤ µ ≤ 65.58 + 2.131⎜⎜ ⎟⎟ ⎝ 16 ⎠ ⎝ 16 ⎠ 63.329 ≤ µ ≤ 67.831 8-35

99% confidence interval on mean current required Assume that the data are a random sample from a normal distribution.

n = 10 x = 317.2 s = 15.7 t 0.005,9 = 3.250 ⎛ s ⎞ ⎛ s ⎞ x − t 0.005,9 ⎜⎜ ⎟⎟ ≤ µ ≤ x + t 0.005,9 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 15.7 ⎞ ⎛ 15.7 ⎞ 317.2 − 3.250⎜⎜ ⎟⎟ ≤ µ ≤ 317.2 + 3.250⎜⎜ ⎟⎟ ⎝ 10 ⎠ ⎝ 10 ⎠ 301.06 ≤ µ ≤ 333.34 a) The data appear to be normally distributed based on examination of the normal probability plot below. Therefore, there is evidence to support that the level of polyunsaturated fatty acid is normally distributed. Normal Probability Plot for 8-25 ML Estimates - 95% CI

99 95 90 80

Percent

8-36

70 60 50 40 30 20 10 5 1 16

17

Data

b) 99% CI on the mean level of polyunsaturated fatty acid. For α = 0.01, t /2,n-1 = t0.005,5 = 4.032 α

⎛ s ⎞ ⎛ s ⎞ ⎟⎟ ≤ µ ≤ x + t 0.005,5 ⎜⎜ ⎟⎟ x − t 0.005,5 ⎜⎜ ⎝ n ⎠ ⎝ n ⎠ ⎛ 0.319 ⎞ ⎛ 0.319 ⎞ 16.98 − 4.032⎜⎜ ⎟⎟ ≤ µ ≤ 16.98 + 4.032⎜⎜ ⎟⎟ ⎝ 6 ⎠ ⎝ 6 ⎠ 16.455 ≤ µ ≤ 17.505

8-10

18

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

The 99% confidence for the mean polyunsaturated fat is (16.455, 17.505). There is high confidence that the true mean is in this interval 8-37

a) The data appear to be normally distributed based on examination of the normal probability plot below. b) 95% two-sided confidence interval on mean comprehensive strength Normal Probability Plot for Strength ML Estimates - 95% CI

99

ML Estimates

95 90

Mean

2259.92

StDev

34.0550

Percent

80 70 60 50 40 30 20 10 5 1 2150

2250

2350

Data

n = 12 x = 2259.9 s = 35.6 t 0.025,11 = 2.201 ⎛ s ⎞ ⎛ s ⎞ x − t 0.025,11 ⎜⎜ ⎟⎟ ≤ µ ≤ x + t 0.025,11 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 35.6 ⎞ ⎛ 35.6 ⎞ 2259.9 − 2.201⎜ ⎟ ≤ µ ≤ 2259.9 + 2.201⎜ ⎟ ⎝ 12 ⎠ ⎝ 12 ⎠ 2237.3 ≤ µ ≤ 2282.5 c) 95% lower-confidence bound on mean strength

⎛ s ⎞ x − t 0.05,11 ⎜⎜ ⎟⎟ ≤ µ ⎝ n ⎠ ⎛ 35.6 ⎞ 2259.9 − 1.796⎜ ⎟ ≤ µ ⎝ 12 ⎠ 2241.4 ≤ µ 8-38

a) According to the normal probability plot there does not seem to be a severe deviation from normality for this data. This is due to the fact that the data appears to fall along a straight line.

8-11

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

Normal Probability Plot for 8-27 ML Estimates - 95% CI

99 95 90

Percent

80 70 60 50 40 30 20 10 5 1 8.15

8.20

8.25

8.30

Data

b) 95% two-sided confidence interval on mean rod diameter For α = 0.05 and n = 15, t /2,n-1 = t0.025,14 = 2.145 α

⎛ s ⎞ ⎛ s ⎞ ⎟⎟ ≤ µ ≤ x + t 0.025,14 ⎜⎜ ⎟⎟ x − t 0.025,14 ⎜⎜ ⎝ n ⎠ ⎝ n ⎠ ⎛ 0.025 ⎞ ⎛ 0.025 ⎞ 8.23 − 2.145⎜ ⎟ ≤ µ ≤ 8.23 + 2.145⎜ ⎟ ⎝ 15 ⎠ ⎝ 15 ⎠ 8.216 ≤ µ ≤ 8.244 c) 95% upper confidence bound on mean rod diameter t0.05,14 = 1.761

⎛ s ⎞ ⎟⎟ ⎝ n ⎠ ⎛ 0.025 ⎞ µ ≤ 8.23 + 1.761⎜⎜ ⎟⎟ ⎝ 15 ⎠ µ ≤ 8.241

µ ≤ x + t 0.025,14 ⎜⎜

8-39

a) The data appear to be normally distributed based on examination of the normal probability plot below. Therefore, there is evidence to support that the speed-up of CNN is normally distributed.

8-12

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

Probability Plot of Speed Normal - 95% CI

99

Mean StDev N AD P-Value

95 90

4.313 0.4328 13 0.233 0.745

Percent

80 70 60 50 40 30 20 10 5

1

3.0

3.5

4.0

4.5 Speed

5.0

5.5

6.0

b) 95% confidence interval on mean speed-up

n = 13 x = 4.313 s = 0.4328 t 0.025,12 = 2.179 ⎛ s ⎞ ⎛ s ⎞ x − t 0.025,12 ⎜⎜ ⎟⎟ ≤ µ ≤ x + t 0.025,12 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 0.4328 ⎞ ⎛ 0.4328 ⎞ 4.313 − 2.179⎜⎜ ⎟⎟ ≤ µ ≤ 4.313 + 2.179⎜⎜ ⎟⎟ ⎝ 13 ⎠ ⎝ 13 ⎠ 4.051 ≤ µ ≤ 4.575 c) 95% lower confidence bound on mean speed-up

n = 13 x = 4.313 s = 0.4328 t 0.05,12 = 1.782 ⎛ s ⎞ x − t 0.05,12 ⎜⎜ ⎟⎟ ≤ µ ⎝ n ⎠ ⎛ 0.4328 ⎞ 4.313 − 1.782⎜⎜ ⎟⎟ ≤ µ ⎝ 13 ⎠ 4.099 ≤ µ 8-40

95% lower bound confidence for the mean wall thickness given x = 4.05 s = 0.08 n = 25 t ,n-1 = t0.05,24 = 1.711 α

⎛ s ⎞ x − t 0.05, 24 ⎜⎜ ⎟⎟ ≤ µ ⎝ n ⎠ ⎛ 0.08 ⎞ 4.05 − 1.711⎜⎜ ⎟⎟ ≤ µ ⎝ 25 ⎠ 4.023 ≤ µ There is high confidence that the true mean wall thickness is greater than 4.023 mm.

8-13

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

Normal Probability Plot for Percent Enrichment ML Estimates - 95% CI

99

ML Estimates

95 90

Mean

2.90167

StDev

0.0951169

Percent

80 70 60 50 40 30 20 10 5 1 2.6

2.7

2.8

2.9

3.0

3.1

3.2

Data

8-41

a) The data appear to be normally distributed. There is not strong evidence that the percentage of enrichment deviates from normality. b) 99% two-sided confidence interval on mean percentage enrichment For α = 0.01 and n = 12, t /2,n-1 = t0.005,11 = 3.106, x = 2.9017 s α

= 0.0993

⎛ s ⎞ ⎛ s ⎞ x − t 0.005,11 ⎜⎜ ⎟⎟ ≤ µ ≤ x + t 0.005,11 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 0.0993 ⎞ ⎛ 0.0993 ⎞ 2.902 − 3.106⎜ ⎟ ≤ µ ≤ 2.902 + 3.106⎜ ⎟ ⎝ 12 ⎠ ⎝ 12 ⎠ 2.813 ≤ µ ≤ 2.991

8-14

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

Section 8-3 8-42

χ 02.05,10 = 18.31

χ 02.025,15 = 27.49

χ 02.01,12 = 26.22

χ 02.95, 20 = 10.85

χ 02.99,18 = 7.01

χ 02.995,16 = 5.14

χ 02.005, 25 = 46.93 8-43

a) 95% upper CI and df = 24

χ12−α ,df = χ 02.95,24 = 13.85

b) 99% lower CI and df = 9

χα2,df = χ 02.01,9 = 21.67

c) 90% CI and df = 19

χα2 / 2,df = χ 02.05,19 = 30.14 8-44

and

χ12−α / 2,df = χ 02.95,19 = 10.12

99% lower confidence bound for σ2 For α = 0.01 and n = 15,

χ α2,n −1 = χ 02.01,14 = 29.14

14(0.008) 2 ≤σ2 29.14 0.00003075 ≤ σ 2 8-45

99% lower confidence bound for σ from the previous exercise is

0.00003075 ≤ σ 2 0.005545 ≤ σ

One may take the square root of the variance bound to obtain the confidence bound for the standard deviation. 8-46

95% two sided confidence interval for σ

n = 10 s = 4.8 χα2 / 2,n−1 = χ 02.025,9 = 19.02

and

χ12−α / 2,n−1 = χ 02.975,9 = 2.70

9( 4.8) 2 9( 4.8) 2 ≤σ2 ≤ 19.02 2.70 2 10.90 ≤ σ ≤ 76.80 3.30 < σ < 8.76 8-47

95% confidence interval for σ: given n = 51, s = 0.37 First find the confidence interval for σ2 : 2 2 2 2 For α = 0.05 and n = 51, χα/ 2, n−1 = χ0.025,50 = 71.42 and χ1− α / 2,n −1 = χ0.975,50 = 32.36

50(0.37) 2 50(0.37) 2 ≤σ2 ≤ 71.42 32.36

0.096 ≤ σ2 ≤ 0.2115 Taking the square root of the endpoints of this interval we obtain, 0.31 < σ < 0.46 8-48

95% confidence interval for σ

n = 17 s = 0.09

8-15

Applied Statistics and Probability for Engineers, 5th edition

χα2 / 2,n−1 = χ 02.025,16 = 28.85

and

September 22, 2010

χ12−α / 2,n−1 = χ 02.975,16 = 6.91

16(0.09) 2 16(0.09) 2 ≤σ2 ≤ 28.85 6.91 2 0.0045 ≤ σ ≤ 0.0188 0.067 < σ < 0.137 8-49

The data appear to be normally distributed based on examination of the normal probability plot below. Therefore, there is evidence to support that the mean temperature is normally distributed.

Probability Plot of Mean Temp Normal - 95% CI

99

Mean StDev N AD P-Value

95 90

Percent

80 70 60 50 40 30 20 10 5

1

17

18

19

20

21 22 Mean Temp

23

24

25

95% confidence interval for σ

n = 8 s = 0.9463 χα2 / 2,n−1 = χ 02.025,7 = 16.01 and χ12−α / 2,n−1 = χ 02.975,7 = 1.69 7(0.9463) 2 7(0.9463) 2 2 ≤σ ≤ 16.01 1.69 2 0.392 ≤ σ ≤ 3.709 0.626 < σ < 1.926

8-50

95% confidence interval for σ

n = 41 s = 15.99 χα2 / 2,n−1 = χ 02.025, 40 = 59.34 and χ12−α / 2,n−1 = χ 02.975, 40 = 24.43 40(15.99) 2 40(15.99) 2 2 ≤σ ≤ 59.34 24.43 2 172.35 ≤ σ ≤ 418.633 13.13 < σ < 20.46

8-16

21.41 0.9463 8 0.352 0.367

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

The data don’t appear to be normally distributed based on examination of the normal probability plot below. Therefore, there is not enough evidence to support that the time of tumor appearance is normally distributed. So the 95% confidence interval for σ is invalid.

Probability Plot of TimeOfTumor Normal - 95% CI

99

Mean StDev N AD P-Value

95 90

88.78 15.99 41 1.631 <0.005

Percent

80 70 60 50 40 30 20 10 5

1

60

80 100 TimeOfTumor

120

140

95% confidence interval for σ

n = 15 s = 0.00831 χα2 / 2,n−1 = χ 02.025,14 = 26.12

and

χ12−α ,n−1 = χ 02.95,14 = 6.53

14(0.00831) 2 6.53 2 σ ≤ 0.000148 σ ≤ 0.0122

σ2 ≤

The data do not appear to be normally distributed based on an examination of the normal probability plot below. Therefore, the 95% confidence interval for σ is not valid.

Probability Plot of Gauge Cap Normal - 95% CI

99

Mean StDev N AD P-Value

95 90 80

Percent

8-51

40

70 60 50 40 30 20 10 5

1

3.44

3.45

3.46

3.47 3.48 Gauge Cap

3.49

8-17

3.50

3.472 0.008307 15 0.878 0.018

Applied Statistics and Probability for Engineers, 5th edition

8-52

September 22, 2010

a) 99% two-sided confidence interval on σ2

n = 10

s = 1.913 χ 02.005,9 = 23.59

and

χ 02.995,9 = 1.73

9(1.913) 2 9(1.913) 2 ≤σ2 ≤ 23.59 1.73 2 1.396 ≤ σ ≤ 19.038 b) 99% lower confidence bound for σ2 For α = 0.01 and n = 10,

χ α2,n −1 = χ 02.01,9 = 21.67

9(1.913) 2 ≤σ2 21.67 1.5199 ≤ σ 2 c) 90% lower confidence bound for σ2 For α = 0.1 and n = 10,

χ α2,n −1 = χ 02.1,9 = 14.68

9(1.913) 2 ≤σ2 14.68 2.2436 ≤ σ 2 1.498 ≤ σ d) The lower confidence bound of the 99% two-sided interval is less than the one-sided interval. The lower confidence bound for σ2 is in part (c) is greater because the confidence is lower. Section 8-4 8-53

a) 95% Confidence Interval on the fraction defective produced with this tool.

pˆ =

13 zα / 2 = 1.96 = 0.04333 n = 300 300 pˆ (1 − pˆ ) pˆ (1 − pˆ ) pˆ − zα / 2 ≤ p ≤ pˆ + zα / 2 n n

0.04333(0.95667) 0.04333(0.95667) ≤ p ≤ 0.04333 + 1.96 300 300 0.02029 ≤ p ≤ 0.06637 b) 95% upper confidence bound zα = z 0.05 = 1.65 0.04333 − 1.96

p ≤ pˆ + zα / 2

pˆ (1 − pˆ ) n

p ≤ 0.04333 + 1.650

0.04333(0.95667) 300

p ≤ 0.06273

8-18

Applied Statistics and Probability for Engineers, 5th edition

8-54

September 22, 2010

a) 95% Confidence Interval on the proportion of such tears that will heal.

pˆ = 0.676 n = 37 zα / 2 = 1.96 pˆ (1 − pˆ ) ≤ p ≤ pˆ + zα / 2 n

pˆ − zα / 2 0.676 − 1.96

pˆ (1 − pˆ ) n

0.676(0.324) 0.676(0.324) ≤ p ≤ 0.676 + 1.96 37 37 0.5245 ≤ p ≤ 0.827

b) 95% lower confidence bound on the proportion of such tears that will heal.

pˆ − zα 0.676 − 1.64

8-55

pˆ (1 − pˆ ) ≤p n

0.676(0.33) ≤p 37 0.549 ≤ p

a) 95% confidence interval for the proportion of college graduates in Ohio that voted for George Bush.

pˆ =

412 = 0.536 768

n = 768 zα / 2 = 1.96

pˆ − zα / 2

pˆ (1 − pˆ ) ≤ p ≤ pˆ + zα / 2 n

0.536 − 1.96

pˆ (1 − pˆ ) n

0.536(0.464) 0.536(0.464) ≤ p ≤ 0.536 + 1.96 768 768 0.501 ≤ p ≤ 0.571

b) 95% lower confidence bound on the proportion of college graduates in Ohio that voted for George Bush.

pˆ − zα 0.536 − 1.64

8-56

pˆ (1 − pˆ ) ≤p n

0.536(0.464) ≤p 768 0.506 ≤ p

a) 95% Confidence Interval on the death rate from lung cancer.

pˆ =

823 = 0.823 n = 1000 zα / 2 = 1.96 1000

8-19

Applied Statistics and Probability for Engineers, 5th edition

pˆ − zα / 2 0.823 − 1.96

pˆ (1 − pˆ ) ≤ p ≤ pˆ + zα / 2 n

September 22, 2010

pˆ (1 − pˆ ) n

0.823(0.177) 0.823(0.177) ≤ p ≤ 0.823 + 1.96 1000 1000 0.7993 ≤ p ≤ 0.8467

b) E = 0.03, α = 0.05, z /2 = z0.025 = 1.96 and p = 0.823 as the initial estimate of p, α

2

2

⎛ z ⎞ ⎛ 1.96 ⎞ n = ⎜ α / 2 ⎟ pˆ (1 − pˆ ) = ⎜ ⎟ 0.823(1 − 0.823) = 621.79 , ⎝ 0.03 ⎠ ⎝ E ⎠ n ≅ 622. c) E = 0.03, α = 0.05, z /2 = z0.025 = 1.96 at least 95% confident α

2

2

⎛ z ⎞ ⎛ 1.96 ⎞ n = ⎜ α / 2 ⎟ (0.25) = ⎜ ⎟ (0.25) = 1067.11 , ⎝ 0.03 ⎠ ⎝ E ⎠ n ≅ 1068. 8-57

a) 95% Confidence Interval on the proportion of rats that are under-weight.

12 = 0.4 n = 30 zα / 2 = 1.96 30 pˆ (1 − pˆ ) pˆ − zα / 2 ≤ p ≤ pˆ + zα / 2 n

pˆ =

0.4 − 1.96

pˆ (1 − pˆ ) n

0.4(0.6) 0.4(0.6) ≤ p ≤ 0.4 + 1.96 30 30 0.225 ≤ p ≤ 0.575

b) E = 0.02, α = 0.05, z /2 = z0.025 = 1.96 and p = 0.4as the initial estimate of p, α

2

2

⎛ z ⎞ ⎛ 1.96 ⎞ n = ⎜ α / 2 ⎟ pˆ (1 − pˆ ) = ⎜ ⎟ 0.4(1 − 0.4) = 2304.96 , ⎝ 0.02 ⎠ ⎝ E ⎠ n ≅ 2305. c) E = 0.02, α = 0.05, z /2 = z0.025 = 1.96 at least 95% confident α

2

2

⎛ z ⎞ ⎛ 1.96 ⎞ n = ⎜ α / 2 ⎟ (0.25) = ⎜ ⎟ (0.25) = 2401. ⎝ 0.02 ⎠ ⎝ E ⎠ 8-58

a) 95% Confidence Interval on the true proportion of helmets showing damage

pˆ =

18 = 0.36 50

zα / 2 = 1.96

n = 50

8-20

Applied Statistics and Probability for Engineers, 5th edition

pˆ − zα / 2

pˆ (1 − pˆ ) ≤ p ≤ pˆ + zα / 2 n

pˆ (1 − pˆ ) n

0.36(0.64) 0.36(0.64) ≤ p ≤ 0.36 + 1.96 50 50 0.227 ≤ p ≤ 0.493

0.36 − 1.96

2

b)

September 22, 2010

2

⎛ z ⎞ ⎛ 1.96 ⎞ n = ⎜ α / 2 ⎟ p(1 − p) = ⎜ ⎟ 0.36(1 − 0.36) = 2212.76 ⎝ 0.02 ⎠ ⎝ E ⎠ n ≅ 2213 2

2

⎛ z ⎞ ⎛ 1.96 ⎞ c) n = ⎜ α / 2 ⎟ p(1 − p) = ⎜ ⎟ 0.5(1 − 0.5) = 2401 ⎝ 0.02 ⎠ ⎝ E ⎠ 8-59

The worst case would be for p = 0.5, thus with E = 0.05 and α = 0.01, z /2 = z0.005 = 2.58 we obtain a sample size of: α

2

2

⎛ z ⎞ ⎛ 2.58 ⎞ n = ⎜ α / 2 ⎟ p(1 − p) = ⎜ ⎟ 0.5(1 − 0.5) = 665.64 , n ≅ 666 ⎝ E ⎠ ⎝ 0.05 ⎠ 8-60

E = 0.017, α = 0.01, z /2 = z0.005 = 2.58 α

2

2

⎛ z ⎞ ⎛ 2.58 ⎞ n = ⎜ α / 2 ⎟ p (1 − p ) = ⎜ ⎟ 0.5(1 − 0.5) = 5758.13 , n ≅ 5759 ⎝ E ⎠ ⎝ 0.017 ⎠ Section 8-6 8-61

95% prediction interval on the life of the next tire given x = 60139.7 s = 3645.94 n = 16 for α=0.05 t /2,n-1 = t0.025,15 = 2.131 α

x − t 0.025,15 s 1 +

1 1 ≤ x n +1 ≤ x + t 0.025,15 s 1 + n n

1 1 ≤ x n +1 ≤ 60139.7 + 2.131(3645.94) 1 + 16 16 52131.1 ≤ x n +1 ≤ 68148.3

60139.7 − 2.131(3645.94) 1 +

The prediction interval is considerably wider than the 95% confidence interval (58,197.3 ≤ µ ≤ 62,082.07). This is expected because the prediction interval needs to include the variability in the parameter estimates as well as the variability in a future observation. 8-62

99% prediction interval on the Izod impact data

8-21

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

n = 20 x = 1.25 s = 0.25 t 0.005,19 = 2.861 x − t 0.005,19 s 1 +

1 1 ≤ x n +1 ≤ x + t 0.005,19 s 1 + n n

1 1 ≤ x n +1 ≤ 1.25 + 2.861(0.25) 1 + 20 20 0.517 ≤ x n +1 ≤ 1.983

1.25 − 2.861(0.25) 1 +

The lower bound of the 99% prediction interval is considerably lower than the 99% confidence interval (1.108 ≤ µ ≤ ∞). This is expected because the prediction interval needs to include the variability in the parameter estimates as well as the variability in a future observation. 8-63

95% Prediction Interval on the volume of syrup of the next beverage dispensed x = 1.10 s = 0.015 n = 25 t /2,n-1 = t0.025,24 = 2.064 α

x − t 0.025, 24 s 1 +

1 1 ≤ x n +1 ≤ x + t 0.025, 24 s 1 + n n

1 1 ≤ x n +1 ≤ 1.10 − 2.064(0.015) 1 + 25 25 1.068 ≤ x n +1 ≤ 1.13

1.10 − 2.064(0.015) 1 +

The prediction interval is wider than the confidence interval: 1.094 ≤ 8-64

µ ≤ 1.106

90% prediction interval the value of the natural frequency of the next beam of this type that will be tested. given x = 231.67, s =1.53 For α = 0.10 and n = 5, t /2,n-1 = t0.05,4 = 2.132 α

x − t 0.05, 4 s 1 +

1 1 ≤ x n +1 ≤ x + t 0.05, 4 s 1 + n n

1 1 ≤ x n +1 ≤ 231.67 − 2.132(1.53) 1 + 5 5 228.1 ≤ x n +1 ≤ 235.2

231.67 − 2.132(1.53) 1 +

The 90% prediction in interval is greater than the 90% CI. 8-65

95% Prediction Interval on the volume of syrup of the next beverage dispensed t /2,n-1 = t0.025,19 = 2.093

n = 20 x = 485.8 s = 90.34 x − t 0.025,19 s 1 +

α

1 1 ≤ x n +1 ≤ x + t 0.025,19 s 1 + n n

1 1 ≤ x n +1 ≤ 485.8 − 2.093(90.34) 1 + 20 20 292.049 ≤ x n +1 ≤ 679.551

485.8 − 2.093(90.34) 1 +

8-66

The 95% prediction interval is wider than the 95% confidence interval. 99% prediction interval on the polyunsaturated fat

8-22

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

n = 6 x = 16.98 s = 0.319 t 0.005,5 = 4.032 x − t 0.005,5 s 1 +

1 1 ≤ x n +1 ≤ x + t 0.005,5 s 1 + n n

1 1 ≤ x n +1 ≤ 16.98 + 4.032(0.319) 1 + 6 6 15.59 ≤ x n +1 ≤ 18.37

16.98 − 4.032(0.319) 1 +

The length of the prediction interval is much longer than the width of the confidence interval 16.455 ≤ µ ≤ 17.505 . 8-67

Given x = 317.2 s = 15.7 n = 10 for α=0.05 t /2,n-1 = t0.005,9 = 3.250 α

x − t 0.005,9 s 1 +

1 1 ≤ x n +1 ≤ x + t 0.005,9 s 1 + n n

1 1 ≤ x n +1 ≤ 317.2 − 3.250(15.7) 1 + 10 10 263.7 ≤ x n +1 ≤ 370.7

317.2 − 3.250(15.7) 1 +

The length of the prediction interval is longer. 8-68

95% prediction interval on the next rod diameter tested

n = 15 x = 8.23 s = 0.025 t 0.025,14 = 2.145 x − t 0.025,14 s 1 +

1 1 ≤ x n +1 ≤ x + t 0.025,14 s 1 + n n

1 1 ≤ x n +1 ≤ 8.23 − 2.145(0.025) 1 + 15 15 8.17 ≤ x n +1 ≤ 8.29

8.23 − 2.145(0.025) 1 +

95% two-sided confidence interval on mean rod diameter is 8.216 ≤ µ ≤ 8.244 8-69

90% prediction interval on the next specimen of concrete tested given x = 2260 s = 35.57 n = 12 for α = 0.05 and n = 12, t /2,n-1 = t0.05,11 = 1.796 α

x − t 0.05,11 s 1 +

1 1 ≤ x n +1 ≤ x + t 0.05,11 s 1 + n n

1 1 ≤ x n +1 ≤ 2260 + 1.796(35.57 ) 1 + 12 12 2193.5 ≤ x n +1 ≤ 2326.5

2260 − 1.796(35.57) 1 +

8-70

90% prediction interval on wall thickness on the next bottle tested.

8-23

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

given x = 4.05 s = 0.08 n = 25 for t /2,n-1 = t0.05,24 = 1.711 α

x − t 0.05, 24 s 1 +

1 1 ≤ x n +1 ≤ x + t 0.05, 24 s 1 + n n

1 1 ≤ x n +1 ≤ 4.05 − 1.711(0.08) 1 + 25 25 3.91 ≤ x n +1 ≤ 4.19

4.05 − 1.711(0.08) 1 +

8-71

90% prediction interval for enrichment data given x = 2.9 s = 0.099 n = 12 for α = 0.10 and n = 12, t /2,n-1 = t0.05,11 = 1.796 α

x − t 0.05,12 s 1 +

1 1 ≤ x n +1 ≤ x + t 0.05,12 s 1 + n n

1 1 ≤ x n +1 ≤ 2.9 + 1.796(0.099) 1 + 12 12 2.71 ≤ x n +1 ≤ 3.09

2.9 − 1.796(0.099) 1 +

The 90% confidence interval is

x − t 0.05,12 s 2.9 − 1.796(0.099)

1 1 ≤ µ ≤ x + t 0.05,12 s n n

1 1 ≤ µ ≤ 2.9 − 1.796(0.099) 12 12 2.85 ≤ µ ≤ 2.95

The prediction interval is wider than the CI on the population mean with the same confidence. The 99% confidence interval is

x − t 0.005,12 s 2.9 − 3.106(0.099)

1 1 ≤ µ ≤ x + t 0.005,12 s n n

1 1 ≤ µ ≤ 2.9 + 3.106(0.099) 12 12 2.81 ≤ µ ≤ 2.99

The prediction interval is even wider than the CI on the population mean with greater confidence.

8-72

To obtain a one sided prediction interval, use t ,n-1 instead of t /2,n-1 Since we want a 95% one sided prediction interval, t /2,n-1 = t0.05,24 = 1.711 and x = 4.05 s = 0.08 n = 25 α

α

α

x − t 0.05, 24 s 1 +

1 ≤ x n +1 n

1 ≤ x n +1 25 3.91 ≤ x n +1

4.05 − 1.711(0.08) 1 +

The prediction interval bound is much lower than the confidence interval bound of

8-24

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

4.023 mm 8-73

95% tolerance interval on the life of the tires that has a 95% CL given x = 60139.7 s = 3645.94 n = 16 we find k=2.903

x − ks , x + ks 60139.7 − 2.903(3645.94 ), 60139.7 + 2.903(3645.94 ) (49555.54, 70723.86) 95% confidence interval (58,197.3 ≤ µ ≤ 62,082.07) is shorter than the 95%tolerance interval. 8-74

99% tolerance interval on the Izod impact strength PVC pipe that has a 90% CL given x=1.25, s=0.25 and n=20 we find k=3.368

x − ks , x + ks 1.25 − 3.368(0.25 ), 1.25 + 3.368(0.25 ) (0.408, 2.092) The 99% tolerance interval is much wider than the 99% confidence interval on the population mean (1.090 ≤ µ ≤ 1.410). 8-75

95% tolerance interval on the syrup volume that has 90% confidence level x = 1.10 s = 0.015 n = 25 and k=2.474

x − ks , x + ks 1.10 − 2.474(0.015), 1.10 + 2.474(0.015) (1.06, 1.14) 8-76

99% tolerance interval on the polyunsaturated fatty acid in this type of margarine that has a confidence level of 95% x = 16.98 s = 0.319 n=6 and k = 5.775

x − ks , x + ks

16.98 − 5.775(0.319), 16.98 + 5.775(0.319) (15.14, 18.82) The 99% tolerance interval is much wider than the 99% confidence interval on the population mean (16.46 ≤ µ ≤ 17.51). 8-77

95% tolerance interval on the rainfall that has a confidence level of 95%

n = 20 x = 485.8 s = 90.34 k = 2.752 x − ks , x + ks 485.8 − 2.752(90.34 ), 485.8 + 2.752(90.34 ) (237.184, 734.416) The 95% tolerance interval is much wider than the 95% confidence interval on the population mean ( 443.52 ≤ µ ≤ 528.08 ). 8-78

95% tolerance interval on the diameter of the rods in exercise 8-27 that has a 90% confidence level x = 8.23 s = 0.0.25 n=15 and k=2.713

8-25

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

x − ks , x + ks 8.23 − 2.713(0.025), 8.23 + 2.713(0.025) (8.16, 8.30) The 95% tolerance interval is wider than the 95% confidence interval on the population mean (8.216 ≤ µ ≤ 8.244). 8-79

99% tolerance interval on the brightness of television tubes that has a 95% CL given x = 317.2 s = 15.7 n = 10 we find k=4.433

x − ks , x + ks 317.2 − 4.433(15.7 ), 317.2 + 4.433(15.7 ) (247.60, 386.80) The 99% tolerance interval is much wider than the 95% confidence interval on the population mean 301.06 ≤ µ ≤ 333.34 . 8-80

90% tolerance interval on the comprehensive strength of concrete that has a 90% CL given x = 2260 s = 35.57 n = 12 we find k=2.404

x − ks , x + ks 2260 − 2.404(35.57 ), 2260 + 2.404(35.57 ) (2174.5, 2345.5) The 90% tolerance interval is much wider than the 95% confidence interval on the population mean 2237.3 ≤ µ ≤ 2282.5. 8-81

99% tolerance interval on rod enrichment data that have a 95% CL given x = 2.9 s = 0.099 n = 12 we find k=4.150

x − ks , x + ks 2.9 − 4.150(0.099), 2.9 + 4.150(0.099) (2.49, 3.31) The 99% tolerance interval is much wider than the 95% CI on the population mean (2.84 ≤ µ ≤ 2.96) 8-82

a) 90% tolerance interval on wall thickness measurements that have a 90% CL given x = 4.05 s = 0.08 n = 25 we find k=2.077

x − ks , x + ks 4.05 − 2.077(0.08 ), 4.05 + 2.077(0.08 ) (3.88, 4.22) The lower bound of the 90% tolerance interval is much lower than the lower bound on the 95% confidence interval on the population mean (4.023 ≤ µ ≤ ∞)

8-26

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

b) 90% lower tolerance bound on bottle wall thickness that has confidence level 90%. given x = 4.05 s = 0.08 n = 25 and k = 1.702

x − ks 4.05 − 1.702(0.08 ) 3.91 The lower tolerance bound is of interest if we want to make sure the wall thickness is at least a certain value so that the bottle will not break.

8-27

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

Supplemental Exercises 8-83

α1 + α 2 = α . Let α = 0.05 Interval for α1 = α 2 = α / 2 = 0.025 The confidence level for x − 1.96σ / n ≤ µ ≤ x + 1.96σ / n is determined by the by the value of z0 Where

which is 1.96. From Table III, we find Φ(1.96) = P(Z<1.96) = 0.975 and the confidence level is 95%. Interval for α1 = 0.01, α 2 = 0.04 The confidence interval is x

− 2.33σ / n ≤ µ ≤ x + 1.75σ / n , the confidence level is the same

because α = 0.05 . The symmetric interval does not affect the level of significance; however, it does affect the length. The symmetric interval is shorter in length. 8-84

µ = 50 σ unknown a) n = 16 x = 52 s = 1.5

52 − 50

to =

8 / 16

=1

The P-value for t0 = 1, degrees of freedom = 15, is between 0.1 and 0.25. Thus we would conclude that the results are not very unusual. b) n = 30

to =

52 − 50 = 1.37 8 / 30

The P-value for t0 = 1.37, degrees of freedom = 29, is between 0.05 and 0.1. Thus we conclude that the results are somewhat unusual. c) n = 100 (with n > 30, the standard normal table can be used for this problem)

zo =

52 − 50 = 2.5 8 / 100

The P-value for z0 = 2.5, is 0.00621. Thus we conclude that the results are very unusual. d) For constant values of x and s, increasing only the sample size, we see that the standard error of X decreases and consequently a sample mean value of 52 when the true mean is 50 is more unusual for the larger sample sizes. 8-85

µ = 50, σ 2 = 5 n = 16 find P( s 2 ≥ 7.44) or P( s 2 ≤ 2.56) 15(7.44) ⎞ ⎛ 2 P( S 2 ≥ 7.44) = P⎜ χ152 ≥ ⎟ = 0.05 ≤ P χ15 ≥ 22.32 ≤ 0.10 2 5 ⎝ ⎠ 2 Using Minitab P( S ≥ 7.44) =0.0997 15(2.56) ⎞ ⎛ 2 P( S 2 ≤ 2.56) = P⎜ χ152 ≤ ⎟ = 0.05 ≤ P χ15 ≤ 7.68 ≤ 0.10 5 ⎝ ⎠ 2 Using Minitab P( S ≤ 2.56) =0.064

a) For

(

(

b) For

n = 30 find P( S 2 ≥ 7.44) or P( S 2 ≤ 2.56)

8-28

)

)

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

⎛ 2 29(7.44) ⎞ 2 P( S 2 ≥ 7.44) = P⎜ χ 29 ≥ ⎟ = 0.025 ≤ P χ 29 ≥ 43.15 ≤ 0.05 5 ⎝ ⎠ 2 Using Minitab P( S ≥ 7.44) = 0.044 ⎛ 2 29(2.56) ⎞ 2 P( S 2 ≤ 2.56) = P⎜ χ 29 ≤ ⎟ = 0.01 ≤ P χ 29 ≤ 14.85 ≤ 0.025 5 ⎝ ⎠ 2 Using Minitab P( S ≤ 2.56) = 0.014.

(

)

(

)

n = 71 P( s 2 ≥ 7.44) or P( s 2 ≤ 2.56) 70(7.44) ⎞ ⎛ 2 P( S 2 ≥ 7.44) = P⎜ χ 702 ≥ ⎟ = 0.005 ≤ P χ 70 ≥ 104.16 ≤ 0.01 5 ⎝ ⎠ 2 Using Minitab P( S ≥ 7.44) =0.0051 70(2.56) ⎞ ⎛ 2 P( S 2 ≤ 2.56) = P⎜ χ 702 ≤ ⎟ = P χ 70 ≤ 35.84 ≤ 0.005 5 ⎝ ⎠ 2 Using Minitab P( S ≤ 2.56) < 0.001

c) For

(

(

)

)

d) The probabilities get smaller as n increases. As n increases, the sample variance should approach the population variance; therefore, the likelihood of obtaining a sample variance much larger than the population variance will decrease. e) The probabilities get smaller as n increases. As n increases, the sample variance should approach the population variance; therefore, the likelihood of obtaining a sample variance much smaller than the population variance will decrease. 8-86

a) The data appear to follow a normal distribution based on the normal probability plot since the data fall along a straight line. b) It is important to check for normality of the distribution underlying the sample data since the confidence intervals to be constructed should have the assumption of normality for the results to be reliable (especially since the sample size is less than 30 and the central limit theorem does not apply). c) No, with 95% confidence, we can not infer that the true mean could be 14.05 since this value is not contained within the given 95% confidence interval. d) As with part b, to construct a confidence interval on the variance, the normality assumption must hold for the results to be reliable. e) Yes, it is reasonable to infer that the variance could be 0.35 since the 95% confidence interval on the variance contains this value. f) i) & ii) No, doctors and children would represent two completely different populations not represented by the population of Canadian Olympic hockey players. Because neither doctors nor children were the target of this study or part of the sample taken, the results should not be extended to these groups.

8-87

a) The probability plot shows that the data appear to be normally distributed. Therefore, there is no evidence conclude that the comprehensive strength data are normally distributed. b) 99% lower confidence bound on the mean x = 25.12, s = 8.42, n = 9 t 0.01,8 = 2.896

8-29

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

⎛ s ⎞ x − t 0.01,8 ⎜⎜ ⎟⎟ ≤ µ ⎝ n ⎠ ⎛ 8.42 ⎞ 25.12 − 2.896⎜⎜ ⎟⎟ ≤ µ ⎝ 9 ⎠ 16.99 ≤ µ The lower bound on the 99% confidence interval shows that the mean comprehensive strength is most likely be greater than 16.99 Megapascals. c) 98% two-sided confidence interval on the mean

x = 25.12, s = 8.42, n = 9 t 0.01,8 = 2.896

⎛ s ⎞ ⎛ s ⎞ x − t0.01,8 ⎜ ⎟ ≤ µ ≤ x + t0.01,8 ⎜ ⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 8.42 ⎞ ⎛ 8.42 ⎞ 25.12 − 2.896⎜ ⎟ ≤ µ ≤ 25.12 + 2.896⎜ ⎟ ⎝ 9 ⎠ ⎝ 9 ⎠ 16.99 ≤ µ ≤ 33.25 The bounds on the 98% two-sided confidence interval shows that the mean comprehensive strength will most likely be greater than 16.99 Megapascals and less than 33.25 Megapascals. The lower bound of the 99% one sided CI is the same as the lower bound of the 98% two-sided CI (this is because of the value of α) d) 99% one-sided upper bound on the confidence interval on σ2 comprehensive strength

s = 8.42, s 2 = 70.90

χ 02.99,8 = 1.65

8(8.42) 2 1.65 2 σ ≤ 343.74

σ2 ≤

The upper bound on the 99% confidence interval on the variance shows that the variance of the comprehensive strength is most likely less than 343.74 Megapascals2. e) 98% two-sided confidence interval on σ2 of comprehensive strength

s = 8.42, s 2 = 70.90 χ 02.01,9 = 20.09 χ 02.99,8 = 1.65 8(8.42) 2 8(8.42) 2 ≤σ2 ≤ 20.09 1.65 2 28.23 ≤ σ ≤ 343.74 The bounds on the 98% two-sided confidence-interval on the variance shows that the variance of the comprehensive strength is most likely less than 343.74 Megapascals2 and greater than 28.23 Megapascals2. The upper bound of the 99% one-sided CI is the same as the upper bound of the 98% two-sided CI because value of α for the one-sided example is one-half the value for the two-sided example. f) 98% two-sided confidence interval on the mean

8-30

x = 23, s = 6.31, n = 9 t 0.01,8 = 2.896

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

⎛ s ⎞ ⎛ s ⎞ x − t 0.01,8 ⎜⎜ ⎟⎟ ≤ µ ≤ x + t 0.01,8 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 6.31 ⎞ ⎛ 6.31 ⎞ 23 − 2.896⎜⎜ ⎟⎟ ≤ µ ≤ 23 + 2.896⎜⎜ ⎟⎟ ⎝ 9 ⎠ ⎝ 9 ⎠ 16.91 ≤ µ ≤ 29.09 98% two-sided confidence interval on σ2 comprehensive strength

s = 6.31, s 2 = 39.8 χ 02.01,9 = 20.09 χ 02.99,8 = 1.65 8(39.8) 8(39.8) ≤σ2 ≤ 20.09 1.65 2 15.85 ≤ σ ≤ 192.97

Fixing the mistake decreased the values of the sample mean and the sample standard deviation. Because the sample standard deviation was decreased the widths of the confidence intervals were also decreased. g) The exercise provides s = 8.41 (instead of the sample variance). A 98% two-sided confidence interval on the mean x = 25, s = 8.41, n = 9 t 0.01,8 = 2.896

⎛ s ⎞ ⎛ s ⎞ x − t0.01,8 ⎜ ⎟ ≤ µ ≤ x + t0.01,8 ⎜ ⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 8.41 ⎞ ⎛ 8.41 ⎞ 25 − 2.896⎜ ⎟ ≤ µ ≤ 25 + 2.896⎜ ⎟ ⎝ 9 ⎠ ⎝ 9 ⎠ 16.88 ≤ µ ≤ 33.12 98% two-sided confidence interval on σ2 of comprehensive strength

s = 8.41, s 2 = 70.73 χ 02.01,9 = 20.09 χ 02.99,8 = 1.65 8(8.41) 2 8(8.41) 2 ≤σ2 ≤ 20.09 1.65 2 28.16 ≤ σ ≤ 342.94

Fixing the mistake did not affect the sample mean or the sample standard deviation. They are very close to the original values. The widths of the confidence intervals are also very similar. h) When a mistaken value is near the sample mean, the mistake will not affect the sample mean, standard deviation or confidence intervals greatly. However, when the mistake is not near the sample mean, the value can greatly affect the sample mean, standard deviation and confidence intervals. The farther from the mean, the greater is the effect. 8-88 With σ = 8, the 95% confidence interval on the mean has length of at most 5; the error is then E = 2.5. 2

2

2

2

⎛ z ⎞ 2 ⎛ 1.96 ⎞ a) n = ⎜ 0.025 ⎟ 8 = ⎜ ⎟ 64 = 39.34 = 40 ⎝ 2.5 ⎠ ⎝ 2.5 ⎠ ⎛ z ⎞ 2 ⎛ 1.96 ⎞ b) n = ⎜ 0.025 ⎟ 6 = ⎜ ⎟ 36 = 22.13 = 23 ⎝ 2.5 ⎠ ⎝ 2.5 ⎠

8-31

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

As the standard deviation decreases, with all other values held constant, the sample size necessary to maintain the acceptable level of confidence and the length of the interval, decreases. 8-89

x = 15.33 s = 0.62 n = 20 k = 2.564

a) 95% Tolerance Interval of hemoglobin values with 90% confidence

x − ks , x + ks 15.33 − 2.564(0.62 ), 15.33 + 2.564(0.62 ) (13.74,

16.92)

b) 99% Tolerance Interval of hemoglobin values with 90% confidence k

= 3.368

x − ks , x + ks 15.33 − 3.368(0.62 ), 15.33 + 3.368(0.62 ) (13.24, 17.42) 8-90

95% prediction interval for the next sample of concrete that will be tested. given x = 25.12 s = 8.42 n = 9 for α = 0.05 and n = 9, t /2,n-1 = t0.025,8 = 2.306 α

x − t 0.025,8 s 1 +

1 1 ≤ x n +1 ≤ x + t 0.025,8 s 1 + n n

1 1 ≤ x n +1 ≤ 25.12 + 2.306(8.42) 1 + 9 9 4.65 ≤ x n +1 ≤ 45.59

25.12 − 2.306(8.42) 1 +

a) There is no evidence to reject the assumption that the data are normally distributed. Normal Probability Plot for foam height ML Estimates - 95% CI

99

ML Estimates

95 90

Mean

203.2

StDev

7.11056

80

Percent

8-91

70 60 50 40 30 20 10 5 1 178

188

198

208

218

228

Data

b) 95% confidence interval on the mean

x = 203.20, s = 7.5, n = 10 t 0.025,9 = 2.262

8-32

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

⎛ s ⎞ ⎛ s ⎞ x − t 0.025,9 ⎜⎜ ⎟⎟ ≤ µ ≤ x − t 0.025,9 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 7.50 ⎞ ⎛ 7.50 ⎞ 203.2 − 2.262⎜⎜ ⎟⎟ ≤ µ ≤ 203.2 + 2.262⎜⎜ ⎟⎟ ⎝ 10 ⎠ ⎝ 10 ⎠ 197.84 ≤ µ ≤ 208.56 c) 95% prediction interval on a future sample

1 1 ≤ µ ≤ x − t 0.025,9 s 1 + n n

x − t 0.025,9 s 1 +

1 1 ≤ µ ≤ 203.2 + 2.262(7.50) 1 + 10 10 185.41 ≤ µ ≤ 220.99 d) 95% tolerance interval on foam height with 99% confidence k = 4.265 x − ks , x + ks 203.2 − 2.262(7.50) 1 +

203.2 − 4.265(7.5), 203.2 + 4.265(7.5) (171.21, 235.19) e) The 95% CI on the population mean is the narrowest interval. For the CI, 95% of such intervals contain the population mean. For the prediction interval, 95% of such intervals will cover a future data value. This interval is quite a bit wider than the CI on the mean. The tolerance interval is the widest interval of all. For the tolerance interval, 99% of such intervals will include 95% of the true distribution of foam height. 8-92 a) Normal probability plot for the coefficient of restitution. There is no evidence to reject the assumption that the data are normally distributed. Normal Probability Plot for 8-79 ML Estimates - 95% CI

99 95 90

Percent

80 70 60 50 40 30 20 10 5 1 0.59

0.60

0.61

0.62

0.63

0.64

0.65

0.66

Data

b) 99% CI on the true mean coefficient of restitution x = 0.624, s = 0.013, n = 40 ta/2, n-1 = t0.005, 39 = 2.7079

8-33

Applied Statistics and Probability for Engineers, 5th edition

x − t 0.005,39 0.624 − 2.7079 c)

s

n 0.013

≤ µ ≤ x + t 0.005,39

September 22, 2010

s n

≤ µ ≤ 0.624 + 2.7079

40 0.618 ≤ µ ≤ 0.630

0.013 40

99% prediction interval on the coefficient of restitution for the next baseball that will be tested.

x − t 0.005,39 s 1 +

1 1 ≤ x n +1 ≤ x + t 0.005,39 s 1 + n n

1 1 ≤ x n +1 ≤ 0.624 + 2.7079(0.013) 1 + 40 40 0.588 ≤ x n +1 ≤ 0.660

0.624 − 2.7079(0.013) 1 +

d) 99% tolerance interval on the coefficient of restitution with a 95% level of confidence

( x − ks , x + ks ) (0.624 − 3.213(0.013), 0.624 + 3.213(0.013)) (0.582, 0.666) e) The confidence interval in part (b) is for the population mean and we may interpret this to imply that 99% of such intervals will cover the true population mean. For the prediction interval, 99% of such intervals will cover a future baseball’s coefficient of restitution. For the tolerance interval, 95% of such intervals will cover 99% of the true distribution. 8-93

95% Confidence Interval on the proportion of baseballs with a coefficient of restitution that exceeds 0.635.

8 = 0.2 n = 40 40 pˆ (1 − pˆ ) pˆ − zα ≤p n

pˆ =

0.2 − 1.65

8-94

zα = 1.65

0.2(0.8) ≤p 40 0.0956 ≤ p

a) The normal probability shows that the data are mostly follow the straight line, however, there are some points that deviate from the line near the middle. It is probably safe to assume that the data are normal.

8-34

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

Normal Probability Plot for 8-81 ML Estimates - 95% CI

99 95 90

Percent

80 70 60 50 40 30 20 10 5 1 0

5

10

Data

b) 95% CI on the mean dissolved oxygen concentration x = 3.265, s = 2.127, n = 20 ta/2, n-1 = t0.025, 19 = 2.093

x − t 0.025,19 3.265 − 2.093 c)

s

n 2.127

≤ µ ≤ x + t 0.025,19

s n

≤ µ ≤ 3.265 + 2.093

20 2.270 ≤ µ ≤ 4.260

2.127 20

95% prediction interval on the oxygen concentration for the next stream in the system that will be tested..

x − t 0.025,19 s 1 +

1 1 ≤ x n +1 ≤ x + t 0.025,19 s 1 + n n

1 1 ≤ x n +1 ≤ 3.265 + 2.093(2.127) 1 + 20 20 − 1.297 ≤ x n +1 ≤ 7.827

3.265 − 2.093(2.127) 1 +

d) 95% tolerance interval on the values of the dissolved oxygen concentration with a 99% level of confidence

( x − ks , x + ks ) (3.265 − 3.168(2.127), 3.265 + 3.168(2.127)) (−3.473, 10.003) e) The confidence interval in part (b) is for the population mean and we may interpret this to imply that 95% of such intervals will cover the true population mean. For the prediction interval, 95% of such intervals will cover a future oxygen concentration. For the tolerance interval, 99% of such intervals will cover 95% of the true distribution 8-95

a) There is no evidence to support that the data are not normally distributed. The data points appear to fall along the normal probability line.

8-35

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

Normal Probability Plot for tar content ML Estimates - 95% CI

99

ML Estimates

95 90

Mean

1.529

StDev

0.0556117

Percent

80 70 60 50 40 30 20 10 5 1 1.4

1.5

1.6

1.7

Data

b) 99% CI on the mean tar content x = 1.529, s = 0.0566, n = 30 ta/2, n-1 = t0.005, 29 = 2.756

x − t 0.005, 29 1.529 − 2.756 c)

s

n 0.0566

≤ µ ≤ x + t 0.005, 29

s n

≤ µ ≤ 1.529 + 2.756

30 1.501 ≤ µ ≤ 1.557

0.0566 30

99% prediction interval on the tar content for the next sample that will be tested..

x − t 0.005,19 s 1 +

1 1 ≤ x n +1 ≤ x + t 0.005,19 s 1 + n n

1 1 ≤ x n +1 ≤ 1.529 + 2.756(0.0566) 1 + 30 30 1.370 ≤ x n +1 ≤ 1.688

1.529 − 2.756(0.0566) 1 +

d) 99% tolerance interval on the values of the tar content with a 95% level of confidence

( x − ks , x + ks ) (1.529 − 3.350(0.0566), 1.529 + 3.350(0.0566)) (1.339, 1.719) e) The confidence interval in part (b) is for the population mean and we may interpret this to imply that 95% of such intervals will cover the true population mean. For the prediction interval, 95% of such intervals will cover a future observed tar content. For the tolerance interval, 99% of such intervals will cover 95% of the true distribution 8-96

a) 95% Confidence Interval on the population proportion n=1200 x=8 pˆ = 0.0067 z /2=z0.025=1.96 α

8-36

Applied Statistics and Probability for Engineers, 5th edition

pˆ − z a / 2 0.0067 − 1.96

September 22, 2010

pˆ (1 − pˆ ) ≤ p ≤ pˆ + z a / 2 n

pˆ (1 − pˆ ) n

0.0067(1 − 0.0067) 0.0067(1 − 0.0067) ≤ p ≤ 0.0067 + 1.96 1200 1200 0.0021 ≤ p ≤ 0.0113

b) No, there is not sufficient evidence to support the claim that the fraction of defective units produced is one percent or less at α = 0.05. This is because the upper limit of the control limit is greater than 0.01. 8-97

99% Confidence Interval on the population proportion n=1600 x=8 pˆ = 0.005 z /2=z0.005=2.58 α

pˆ (1 − pˆ ) ≤ p ≤ pˆ + z a / 2 n

pˆ − z a / 2 0.005 − 2.58

pˆ (1 − pˆ ) n

0.005(1 − 0.005) 0.005(1 − 0.005) ≤ p ≤ 0.005 + 2.58 1600 1600 0.0004505 ≤ p ≤ 0.009549

b) E = 0.008, α = 0.01, z /2 = z0.005 = 2.58 α

2

2

⎛ z ⎞ ⎛ 2.58 ⎞ n = ⎜ α / 2 ⎟ p(1 − p) = ⎜ ⎟ 0.005(1 − 0.005) = 517.43 , n ≅ 518 ⎝ 0.008 ⎠ ⎝ E ⎠ c) E = 0.008, α = 0.01, z /2 = z0.005 = 2.58 α

2

2

⎛ z ⎞ ⎛ 2.58 ⎞ n = ⎜ α / 2 ⎟ p(1 − p) = ⎜ ⎟ 0.5(1 − 0.5) = 26001.56 , n ≅ 26002 ⎝ 0.008 ⎠ ⎝ E ⎠

d) A bound on the true population proportion reduces the required sample size by a substantial amount. A sample size of 518 is much more reasonable than a sample size of over 26,000. 8-98

pˆ =

117 = 0.242 484

a) 90% confidence interval;

pˆ − zα / 2

zα / 2 = 1.645

pˆ (1 − pˆ ) ≤ p ≤ pˆ + zα / 2 n 0.210 ≤ p ≤ 0.274

pˆ (1 − pˆ ) n

With 90% confidence, the true proportion of new engineering graduates who were planning to continue studying for an advanced degree is between 0.210 and 0.274. b) 95% confidence interval; zα/ 2 = 196 .

pˆ − zα / 2

pˆ (1 − pˆ ) ≤ p ≤ pˆ + zα / 2 n 0.204 ≤ p ≤ 0.280

pˆ (1 − pˆ ) n

With 95% confidence, we believe the true proportion of new engineering graduates who were planning to continue studying for an advanced degree lies between 0.204 and 0.280. c) Comparison of parts (a) and (b):

8-37

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

The 95% confidence interval is larger than the 90% confidence interval. Higher confidence always yields larger intervals, all other values held constant. d) Yes, since both intervals contain the value 0.25, thus there in not enough evidence to determine that the true proportion is not actually 0.25. 8-99

a) The data appear to follow a normal distribution based on the normal probability plot since the data fall along a straight line. b) It is important to check for normality of the distribution underlying the sample data since the confidence intervals to be constructed should have the assumption of normality for the results to be reliable (especially since the sample size is less than 30 and the central limit theorem does not apply). c) 95% confidence interval for the mean

n = 11 x = 22.73 s = 6.33 t 0.025,10 = 2.228 ⎛ s ⎞ ⎛ s ⎞ x − t 0.025,10 ⎜⎜ ⎟⎟ ≤ µ ≤ x + t 0.025,10 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 6.33 ⎞ ⎛ 6.33 ⎞ 22.73 − 2.228⎜ ⎟ ≤ µ ≤ 22.73 + 2.228⎜ ⎟ ⎝ 11 ⎠ ⎝ 11 ⎠ 18.478 ≤ µ ≤ 26.982 d) As with part b, to construct a confidence interval on the variance, the normality assumption must hold for the results to be reliable. e) 95% confidence interval for variance

n = 11 s = 6.33 χα2 / 2,n−1 = χ 02.025,10 = 20.48

and

χ12−α / 2,n−1 = χ 02.975,10 = 3.25

10(6.33) 2 10(6.33) 2 ≤σ2 ≤ 20.48 3.25 2 19.565 ≤ σ ≤ 123.289 8-100

a) The data appear to be normally distributed based on examination of the normal probability plot below. Therefore, there is evidence to support that the energy intake is normally distributed.

8-38

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

Probability Plot of Energy Normal - 95% CI

99

Mean StDev N AD P-Value

95 90

Percent

80 70 60 50 40 30 20 10 5

1

4

5

6 Energy

7

8

b) 99% upper confidence interval on mean energy (BMR)

n = 10 x = 5.884 s = 0.5645 t 0.005,9 = 3.250 ⎛ s ⎞ ⎛ s ⎞ x − t 0.005,9 ⎜⎜ ⎟⎟ ≤ µ ≤ x + t 0.005,9 ⎜⎜ ⎟⎟ ⎝ n ⎠ ⎝ n ⎠ ⎛ 0.5645 ⎞ ⎛ 0.5645 ⎞ 5.884 − 3.250⎜⎜ ⎟⎟ ≤ µ ≤ 5.884 + 3.250⎜⎜ ⎟⎟ ⎝ 10 ⎠ ⎝ 10 ⎠ 5.304 ≤ µ ≤ 6.464

8-39

5.884 0.5645 10 0.928 0.011

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

Mind Expanding Exercises 8-101

a.)

P( χ 12−α , 2 r < 2λTr < χ α2 , 2 r ) = 1 − α 2

2

2

2

χ α ,2r ⎛ χ 1− α , 2 r = P⎜ 2 < λ < 2 ⎜ 2Tr 2Tr ⎝

⎞ ⎟ ⎟ ⎠

Then a confidence interval for

µ=

1

λ

is

⎛ 2T ⎜ r , 2Tr ⎜ χ α2 χ 2 α ⎝ 2 , 2 r 1− 2 , 2 r

⎞ ⎟ ⎟ ⎠

b) n = 20 , r = 10 , and the observed value of Tr is 199 + 10(29) = 489. A 95% confidence interval for

8-102

∞

1

z α1

2π

α1 = ∫

Therefore,

2

e

− x2

1

is

λ

⎛ 2(489) 2(489) ⎞ , ⎜ ⎟ = (28.62,101.98) ⎝ 34.17 9.59 ⎠

z α1 1 − x22 dx = 1 − ∫ e dx 2 π −∞

1 − α 1 = Φ ( z α1 ) .

Φ −1 (1 − α 1 ) + Φ(1 − α 2 ) Φ −1 (1 − α 1 ) + Φ(1 − α + α 1 ) .

To minimize L we need to minimize need to minimize

∂ Φ −1 (1 − α 1 ) = − 2π e ∂α 1

subject to

zα2

1 2

∂ Φ −1 (1 − α + α 1 ) = 2π e ∂α 1

zα2 −α

1

2

Upon setting the sum of the two derivatives equal to zero, we obtain zα1 = zα − α1 . Consequently,

8.103

α 1 = α − α 1 , 2α 1 = α

a) n=1/2+(1.9/.1)(9.4877/4), then n=46 b) (10-.5)/(9.4877/4)=(1+p)/(1-p) p=0.6004 between 10.19 and 10.41.

8-104

α1 + α 2 = α . Therefore, we

a)

P( X i ≤ µ~) = 1 / 2 P(allX ≤ µ~) = (1 / 2) n i

P(allX i ≥ µ~) = (1 / 2) n

8-40

and

e

zα2 −α 2

α1 = α 2 =

1

=e

α

2

.

zα2

1 2

. This is solved by

Applied Statistics and Probability for Engineers, 5th edition

September 22, 2010

P( A ∪ B) = P( A) + P( B) − P( A ∩ B) n

n

n

⎛ 1 ⎞ ⎛ 1 ⎞ ⎛ 1 ⎞ ⎛ 1 ⎞ = ⎜ ⎟ + ⎜ ⎟ = 2⎜ ⎟ = ⎜ ⎟ ⎝ 2 ⎠ ⎝ 2 ⎠ ⎝ 2 ⎠ ⎝ 2 ⎠

n −1

⎛ 1 ⎞ 1 − P( A ∪ B) = P(min( X i ) < µ~ < max( X i )) = 1 − ⎜ ⎟ ⎝ 2 ⎠ ~ b) P(min( X i ) < µ < max( X i )) = 1 − α

n

The confidence interval is min(Xi), max(Xi) 8-105

We would expect that 950 of the confidence intervals would include the value of µ. This is due to 9the definition of a confidence interval. Let X bet the number of intervals that contain the true mean (µ). We can use the large sample approximation to determine the probability that P(930 < X < 970). Let

p =

950 930 970 = 0.950 p1 = = 0.930 and p 2 = = 0.970 1000 1000 1000

The variance is estimated by

p(1 − p) 0.950(0.050) = n 1000

⎛ ⎞ ⎛ ⎞ ⎜ ⎟ ⎜ ⎟ (0.970 − 0.950) ⎟ (0.930 − 0.950) ⎟ P(0.930 < p < 0.970) = P⎜ Z < − P⎜ Z < ⎜ ⎜ 0.950(0.050) ⎟ 0.950(0.050) ⎟ ⎜ ⎟ ⎜ ⎟ 1000 1000 ⎝ ⎠ ⎝ ⎠ 0.02 ⎞ − 0.02 ⎞ ⎛ ⎛ = P⎜ Z < ⎟ − P⎜ Z < ⎟ = P( Z < 2.90) − P( Z < −2.90) = 0.9963 0.006892 ⎠ 0.006892 ⎠ ⎝ ⎝

8-41

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