Solution manual for design and analysis of experiments 9th edition douglas c. montgomery

1-1
1.1. Suppose that you want to design an experiment to study the proportion of unpopped kernels of
popcorn. Complete steps 1-3 of the guidelines for designing experiments in Section 1.4. Are there
any major sources of variation that would be difficult to control?
Step 1 – Recognition of and statement of the problem. Possible problem statement would be – find
the best combination of inputs that maximizes yield on popcorn – minimize unpopped kernels.
Step 2 – Selection of the response variable. Possible responses are number of unpopped kernels per
100 kernals in experiment, weight of unpopped kernels versus the total weight of kernels cooked.
Step 3 – Choice of factors, levels and range. Possible factors and levels are brand of popcorn (levels:
cheap, expensive), age of popcorn (levels: fresh, old), type of cooking method (levels: stovetop,
microwave), temperature (levels: 150C, 250C), cooking time (levels: 3 minutes, 5 minutes), amount of
cooking oil (levels, 1 oz, 3 oz), etc.
1.2. Suppose that you want to investigate the factors that potentially affect cooked rice.
(a) What would you use as a response variable in this experiment? How would you measure the
response?
(b) List all of the potential sources of variability that could impact the response.
(c) Complete the first three steps of the guidelines for designing experiments in Section 1.4.
Step 1 – Recognition of and statement of the problem.
Step 2 – Selection of the response variable.
Step 3 – Choice of factors, levels and range.
1.3. Suppose that you want to compare the growth of garden flowers with different conditions of
sunlight, water, fertilizer and soil conditions. Complete steps 1-3 of the guidelines for designing
experiments in Section 1.4.
Step 1 – Recognition of and statement of the problem.
Step 2 – Selection of the response variable.
Step 3 – Choice of factors, levels and range.
1.4. Select an experiment of interest to you. Complete steps 1-3 of the guidelines for designing
experiments in Section 1.4.
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1-3
Experimental runs can be expensive and time consuming. If an error were to occur while running the
experiment, the cost of redoing the experiment is much more manageable with one of the small
sequential experiments than the large comprehensive experiment.
1.11. Have you received an offer to obtain a credit card in the mail? What “factors” were associated
with the offer, such as introductory interest rate? Do you think the credit card company is
conducting experiments to investigate which facors product the highest positive response rate to their
offer? What potential factors in the experiment can you identify?
Interest rate, credit limit, old credit card pay-off amount, interest free period, gift points, others.
1.12. What factors do you think an e-commerce company could use in an experiment involving their
web page to encourage more people to “click-through” into their site?
Font size, font type, images/icons, color, spacing, animation, sound/music, speed, others.

2-1
Chapter 2
Simple Comparative Experiments
Solutions
2.1. Computer output for a random sample of data is shown below. Some of the quantities are
missing. Compute the values of the missing quantities.
Variable N Mean SE Mean Std. Dev. Variance Minimum Maximum
Y 9 19.96 ? 3.12 ? 15.94 27.16
SE Mean = 1.04 Variance = 9.73
2.2. Computer output for a random sample of data is shown below. Some of the quantities are
missing. Compute the values of the missing quantities.
Variable N Mean SE Mean Std. Dev. Sum
Y 16 ? 0.159 ? 399.851
Mean = 24.991 Std. Dev. = 0.636
2.3. Suppose that we are testing H0: μ = μ0 versus H1: μ ≠ μ0. Calculate the P-value for the following
observed values of the test statistic:
(a) Z0 = 2.25 P-value = 0.02445
(b) Z0 = 1.55 P-value = 0.12114
(c) Z0 = 2.10 P-value = 0.03573
(d) Z0 = 1.95 P-value = 0.05118
(e) Z0 = -0.10 P-value = 0.92034
2.4. Suppose that we are testing H0: μ = μ0 versus H1: μ > μ0. Calculate the P-value for the
following observed values of the test statistic:
(a) Z0 = 2.45 P-value = 0.00714
(b) Z0 = -1.53 P-value = 0.93699

2-2
(c) Z0 = 2.15 P-value = 0.01578
(d) Z0 = 1.95 P-value = 0.02559
(e) Z0 = -0.25 P-value = 0.59871
2.5. Consider the computer output shown below.
One-Sample Z
Test of mu = 30 vs not = 30
The assumed standard deviation = 1.2
N Mean SE Mean 95% CI Z P
16 31.2000 0.3000 (30.6120, 31.7880) ? ?
(a) Fill in the missing values in the output. What conclusion would you draw?
Z = 4 P = 0.00006; therefore, the mean is not equal to 30.
(b) Is this a one-sided or two-sided test?
Two-sided.
(c) Use the output and the normal table to find a 99 percent CI on the mean.
CI = 30.42725, 31.97275
(d) What is the P-value if the alternative hypothesis is H1: μ > 30
P-value = 0.00003
2.6. Suppose that we are testing H0: μ1 = μ2 versus H1: μ1 = μ2 with a sample size of n1 = n2 = 12.
Both sample variances are unknown but assumed equal. Find bounds on the P-value for the
(a) t0 = 2.30 Table P-value = 0.02, 0.05 Computer P-value = 0.0313
(b) t0 = 3.41 Table P-value = 0.002, 0.005 Computer P-value = 0.0025
(c) t0 = 1.95 Table P-value = 0.1, 0.05 Computer P-value = 0.0640
(d) t0 = -2.45 Table P-value = 0.05, 0.02 Computer P-value = 0.0227
Note that the degrees of freedom is (12 +12) – 2 = 22. This is a two-sided test
2.7. Suppose that we are testing H0: μ1 = μ2 versus H1: μ1 > μ2 with a sample size of n1 = n2 = 10.
Both sample variances are unknown but assumed equal. Find bounds on the P-value for the

2-3
(d) t0 = 2.19 Table P-value = 0.01, 0.025 Computer P-value = 0.02097
Note that the degrees of freedom is (10 +10) – 2 = 18. This is a one-sided test.
2.8. Consider the following sample data: 9.37, 13.04, 11.69, 8.21, 11.18, 10.41, 13.15, 11.51, 13.21,
and 7.75. Is it reasonable to assume that this data is from a normal distribution? Is there evidence to
support a claim that the mean of the population is 10?
Minitab Output
According to the output, the Anderson-Darling Normality Test has a P-Value of 0.435. The data can
be considered normal. The 95% confidence interval on the mean is (9.526,12.378). This confidence
interval contains 10, therefore there is evidence that the population mean is 10.
2.9. A computer program has produced the following output for the hypothesis testing problem:
Difference in sample means: 2.35
Degrees of freedom: 18
Standard error of the difference in the sample means: ?
Test statistic: to = 2.01
P-Value = 0.0298

2-4
(a) What is the missing value for the standard error?
1 2
0
1 2
2.35
2.01
1 1
2.35/ 2.01 1.169
p
y y
t
StdError
S
n n
StdError
−
= = =
+
= =
(b) Is this a two-sided or one-sided test? One-sided test for a t0 = 2.01 is a P-value of 0.0298.
(c) If α=0.05, what are your conclusions? Reject the null hypothesis and conclude that there is a
difference in the two samples.
(d) Find a 90% two-sided CI on the difference in the means.
( ) ( )
1 2 1 21 2 2, 2 1 1 1 2 2, 2
1 2 1 2
1 2 0.05,18 1 1 1 2 0.05,18
1 2 1 2
1 1
1 1
1 1 1 1
1 1 1 1
2.35 1.734 1.169 2.35 1.734 1.169
0.323 4.377
n n p n n p
p p
y y t S y y t S
n n n n
y y t S y y t S
n n n n
α αμ μ
μ μ
μ μ
μ μ
+ − + −− − + ≤ − ≤ − + +
− − + ≤ − ≤ − + +
− ≤ − ≤ +
≤ − ≤
2.10. A computer program has produced the following output for the hypothesis testing problem:
Difference in sample means: 11.5
Degrees of freedom: 24
Standard error of the difference in the sample means: ?
Test statistic: to = -1.88
P-Value = 0.0723
(a) What is the missing value for the standard error?
1 2
0
1 2
11.5
1.88
1 1
11.5 / 1.88 6.12
p
y y
t
StdError
S
n n
StdError
− −
= = = −
+
= − − =
(b) Is this a two-sided or one-sided test? Two-sided test for a t0 = -1.88 is a P-value of 0.0723.
(c) If α=0.05, what are your conclusions? Accept the null hypothesis, there is no difference in the
means.

2-5
(d) Find a 90% two-sided CI on the difference in the means.
( ) ( )
1 2 1 21 2 2, 2 1 1 1 2 2, 2
1 2 1 2
1 2 0.05,24 1 1 1 2 0.05,24
1 2 1 2
1 1
1 1
1 1 1 1
1 1 1 1
11.5 1.711 6.12 11.5 1.711 6.12
21.97 1.03
n n p n n p
p p
y y t S y y t S
n n n n
y y t S y y t S
n n n n
α αμ μ
μ μ
μ μ
μ μ
+ − + −− − + ≤ − ≤ − + +
− − + ≤ − ≤ − + +
− − ≤ − ≤ − +
− ≤ − ≤ −
2.11. A two-sample t-test has been conducted and the sample sizes are n1 = n2 = 10. The computed
value of the test statistic is t0 = 2.15. If the null hypothesis is two-sided, an upper bound on the P-
value is
(a) 0.10
(b) 0.05
(c) 0.025
(d) 0.01
(e) None of the above.
2.12. A two-sample t-test has been conducted and the sample sizes are n1 = n2 = 12. The computed
value of the test statistic is t0 = 2.27. If the null hypothesis is two-sided, an upper bound on the P-
value is
(a) 0.10
(b) 0.05
(c) 0.025
(d) 0.01
(e) None of the above.
2.13. Suppose that we are testing H0: μ = μ0 versus H1: μ > μ0 with a sample size of n = 15. Calculate
bounds on the P-value for the following observed values of the test statistic:

2-6
The degrees of freedom are 15 – 1 = 14. This is a one-sided test.
2.14. Suppose that we are testing H0: μ = μ0 versus H1: μ ≠ μ0 with a sample size of n = 10. Calculate
bounds on the P-value for the following observed values of the test statistic:
(b) t0 = -3.95 Table P-value = 0.002, 0.005 Computer P-value = 0.00335
(e) t0 = -1.25 Table P-value = 0.20, 0.50 Computer P-value = 0.24282
One-Sample T: Y
Test of mu = 91 vs. not = 91
Variable N Mean Std. Dev. SE Mean 95% CI T P
Y 25 92.5805 ? 0.4675 (91.6160, ? ) 3.38 0.002
(a) Fill in the missing values in the output. Can the null hypothesis be rejected at the 0.05 level?
Why?
Std. Dev. = 2.3365 UCI = 93.5450
Yes, the null hypothesis can be rejected at the 0.05 level because the P-value is much lower at
0.002.
Two-sided.
(c) If the hypothesis had been H0: μ = 90 versus H1: μ ≠ 90 would you reject the null hypothesis at
the 0.05 level?
Yes.
(d) Use the output and the t table to find a 99 percent two-sided CI on the mean.
CI = 91.2735, 93.8875
(e) What is the P-value if the alternative hypothesis is H1: μ > 91?
P-value = 0.001.

2-7
One-Sample T: Y
Test of mu = 25 vs > 25
Variable N Mean Std. Dev. SE Mean 95% Lower Bound T P
Y 12 25.6818 ? 0.3360 ? ? 0.034
(a) How many degrees of freedom are there on the t-test statistic?
(N-1) = (12 – 1) = 11
(b) Fill in the missing information.
Std. Dev. = 1.1639 95% Lower Bound = 2.0292
Two-Sample T-Test and CI: Y1, Y2
Two-sample T for Y1 vs Y2
N Mean Std. Dev. SE Mean
Y1 20 50.19 1.71 0.38
Y2 20 52.52 2.48 0.55
Difference = mu (X1) – mu (X2)
Estimate for difference: -2.33341
95% CI for difference: (-3.69547, -0.97135)
T-Test of difference = 0 (vs not = ) : T-Value = -3.47
P-Value = 0.01 DF = 38
Both use Pooled Std. Dev. = 2.1277
(a) Can the null hypothesis be rejected at the 0.05 level? Why?
Yes, the P-Value of 0.001 is much less than 0.05.
Two-sided.
(c) If the hypothesis had been H0: μ1 - μ2 = 2 versus H1: μ1 - μ2 ≠ 2 would you reject the null
hypothesis at the 0.05 level?
Yes.

2-8
(d) If the hypothesis had been H0: μ1 - μ2 = 2 versus H1: μ1 - μ2 < 2 would you reject the null
hypothesis at the 0.05 level? Can you answer this question without doing any additional
calculations? Why?
Yes, no additional calculations are required because the test is naturally becoming more
significant with the change from -2.33341 to -4.33341.
(e) Use the output and the t table to find a 95 percent upper confidence bound on the difference in
means?
95% upper confidence bound = -1.21.
(f) What is the P-value if the alternative hypotheses are H0: μ1 - μ2 = 2 versus H1: μ1 - μ2 ≠ 2?
P-value = 1.4E-07.
2.18. The breaking strength of a fiber is required to be at least 150 psi. Past experience has indicated
that the standard deviation of breaking strength is σ = 3 psi. A random sample of four specimens is
tested. The results are y1=145, y2=153, y3=150 and y4=147.
(a) State the hypotheses that you think should be tested in this experiment.
H0: μ = 150 H1: μ > 150
(b) Test these hypotheses using α = 0.05. What are your conclusions?
n = 4, σ = 3, y= 1/4 (145 + 153 + 150 + 147) = 148.75
μ
σ
− − −
= = = = −
148.75 150 1.25
0.8333
3 3
24
o
o
y
z
n
Since z0.05 = 1.645, do not reject.
(c) Find the P-value for the test in part (b).
From the z-table: ( )( )≅ − + − =⎡ ⎤⎣ ⎦1 0.7967 2 3 0.7995 0.7967 0.2014P
(d) Construct a 95 percent confidence interval on the mean breaking strength.
The 95% confidence interval is
( )( ) ( )( )
α α
σ σ
μ
μ
− ≤ ≤ +
− ≤ ≤ +
2 2
148.75 1.96 3 2 148.75 1.96 3 2
y z y z
n n
μ≤ ≤145.81 151.69

2-9
2.19. The viscosity of a liquid detergent is supposed to average 800 centistokes at 25°C. A random
sample of 16 batches of detergent is collected, and the average viscosity is 812. Suppose we know that
the standard deviation of viscosity is σ = 25 centistokes.
(a) State the hypotheses that should be tested.
H0: μ = 800 H1: μ ≠ 800
μ
σ
− −
= = = =
812 800 12
1.92
25 25
416
o
o
y
z
n
Since zα/2 = z0.025 = 1.96, do not reject.
(c) What is the P-value for the test?
(d) Find a 95 percent confidence interval on the mean.
α α
σ σ
μ− ≤ ≤ +2 2
y z y z
n n
( )( ) ( )( )μ
μ
μ
− ≤ ≤ +
− ≤ ≤ +
≤ ≤
812 1.96 25 4 812 1.96 25 4
812 12.25 812 12.25
799.75 824.25
2.20. The diameters of steel shafts produced by a certain manufacturing process should have a mean
diameter of 0.255 inches. The diameter is known to have a standard deviation of σ = 0.0001 inch. A
random sample of 10 shafts has an average diameter of 0.2545 inches.
(a) Set up the appropriate hypotheses on the mean μ.
H0: μ = 0.255 H1: μ ≠ 0.255
n = 10, σ = 0.0001, y= 0.2545
μ
σ
− −
= = = −
0.2545 0.255
15.81
0.0001
10
o
o
y
z
n
Since z0.025 = 1.96, reject H0.
(c) Find the P-value for this test. P = 2.6547x10-56

2-10
(d) Construct a 95 percent confidence interval on the mean shaft diameter.
α α
σ σ
μ− ≤ ≤ +2 2
y z y z
n n
( ) ( )μ
⎛ ⎞ ⎛ ⎞
− ≤ ≤ +⎜ ⎟ ⎜ ⎟
⎝ ⎠ ⎝ ⎠
0.0001 0.0001
0.2545 1.96 0.2545 1.96
10 10
μ≤ ≤0.254438 0.254562
2.21. A normally distributed random variable has an unknown mean μ and a known variance σ2 = 9.
Find the sample size required to construct a 95 percent confidence interval on the mean that has total
length of 1.0.
Since y ∼ N(μ,9), a 95% two-sided confidence interval on μ is
If the total interval is to have width 1.0, then the half-interval is 0.5. Since zα/2 = z0.025 = 1.96,
( ) ( )
( )
( )
⎛ ⎞
=⎜ ⎟
⎝ ⎠
⎛ ⎞
= =⎜ ⎟
⎝ ⎠
= = ≅
2
3
1.96 0.5 1.96
3
1.96 11.76
0.5
11.76 138.30 139
n
n
n
2.22. The shelf life of a carbonated beverage is of interest. Ten bottles are randomly selected and
tested, and the following results are obtained:
Days
108 138
124 163
124 159
106 134
115 139
(a) We would like to demonstrate that the mean shelf life exceeds 120 days. Set up appropriate
hypotheses for investigating this claim.
H0: μ = 120 H1: μ > 120
y = 131
S2 = 3438 / 9 = 382
= =382 19.54S
μ− −
= = =0
0
131 120
1.78
19.54 10
y
t
S n
since t0.01,9 = 2.821; do not reject H0

2-11
Minitab Output
T-Test of the Mean
Test of mu = 120.00 vs mu > 120.00
Variable N Mean StDev SE Mean T P
Shelf Life 10 131.00 19.54 6.18 1.78 0.054
T Confidence Intervals
Variable N Mean StDev SE Mean 99.0 % CI
Shelf Life 10 131.00 19.54 6.18 ( 110.91, 151.09)
(c) Find the P-value for the test in part (b). P=0.054
(d) Construct a 99 percent confidence interval on the mean shelf life.
The 99% confidence interval is α αμ− −
− ≤ ≤ +, 1 , 1
2 2
n n
S S
y t y t
n n
with α = 0.01.
( ) ( )μ
⎛ ⎞ ⎛ ⎞
− ≤ ≤ +⎜ ⎟ ⎜ ⎟
⎝ ⎠ ⎝ ⎠
19.54 19.54
131 3.250 131 3.250
10 10
μ≤ ≤110.91 151.08
2.23. Consider the shelf life data in Problem 2.22. Can shelf life be described or modeled adequately
by a normal distribution? What effect would violation of this assumption have on the test procedure
you used in solving Problem 2.22?
A normal probability plot, obtained from Minitab, is shown. There is no reason to doubt the
adequacy of the normality assumption. If shelf life is not normally distributed, then the impact of
this on the t-test in problem 2.22 is not too serious unless the departure from normality is severe.

2-12
2.24. The time to repair an electronic instrument is a normally distributed random variable
measured in hours. The repair time for 16 such instruments chosen at random are as follows:
Hours
159 280 101 212
224 379 179 264
222 362 168 250
149 260 485 170
(a) You wish to know if the mean repair time exceeds 225 hours. Set up appropriate hypotheses
for investigating this issue.
H0: μ = 225 H1: μ > 225
(b) Test the hypotheses you formulated in part (a). What are your conclusions? Use α = 0.05.
y= 241.50
S2 =146202 / (16 - 1) = 9746.80
= =9746.8 98.73S
μ− −
= = =
241.50 225
0.67
98.73
16
o
o
y
t
S
n
since t0.05,15 = 1.753; do not reject H0
T-Test of the Mean
Test of mu = 225.0 vs mu > 225.0
Variable N Mean StDev SE Mean T P
Hours 16 241.5 98.7 24.7 0.67 0.26
T Confidence Intervals
Variable N Mean StDev SE Mean 95.0 % CI
Hours 16 241.5 98.7 24.7 ( 188.9, 294.1)
(c) Find the P-value for this test. P=0.26
(d) Construct a 95 percent confidence interval on mean repair time.
The 95% confidence interval is α αμ− −
− ≤ ≤ +, 1 , 1
2 2
n n
S S
y t y t
n n
( ) ( )μ
⎛ ⎞ ⎛ ⎞
− ≤ ≤ +⎜ ⎟ ⎜ ⎟
⎝ ⎠ ⎝ ⎠
98.73 98.73
241.50 2.131 241.50 2.131
16 16
μ≤ ≤188.9 294.1

2-13
2.25. Reconsider the repair time data in Problem 2.24. Can repair time, in your opinion, be
adequately modeled by a normal distribution?
The normal probability plot below does not reveal any serious problem with the normality
assumption.
2.26. Two machines are used for filling plastic bottles with a net volume of 16.0 ounces. The filling
processes can be assumed to be normal, with standard deviation of σ1 = 0.015 and σ2 = 0.018. The
quality engineering department suspects that both machines fill to the same net volume, whether or
not this volume is 16.0 ounces. An experiment is performed by taking a random sample from the
output of each machine.
Machine 1 Machine 2
16.03 16.01 16.02 16.03
16.04 15.96 15.97 16.04
16.05 15.98 15.96 16.02
16.05 16.02 16.01 16.01
16.02 15.99 15.99 16.00
(a) State the hypotheses that should be tested in this experiment.
H0: μ1 = μ2 H1: μ1 ≠ μ2
(b) Test these hypotheses using α=0.05. What are your conclusions?
σ
=
=
=
1
1
1
16.015
0.015
10
y
n
σ
=
=
=
2
2
2
16.005
0.018
10
y
n

2-14
1 2
2 2 2 2
1 2
1 2
16.015 16.018
1.35
0.015 0.018
10 10
o
y y
z
n n
σ σ
− −
= = =
++
z0.025 = 1.96; do not reject
(c) What is the P-value for the test? P = 0.1770
(d) Find a 95 percent confidence interval on the difference in the mean fill volume for the two
machines.
2 2
2 2 2 2
1 2 1 2
1 2 1 2 1 2
1 2 1 2
y y z y y z
n n n n
α α
σ σ σ σ
μ μ− − + ≤ − ≤ − + +
2 2 2 2
1 2
0.015 0.018 0.015 0.018
(16.015 16.005) (1.96) (16.015 16.005) (1.96)
10 10 10 10
μ μ− − + ≤ − ≤ − + +
1 20.0045 0.0245μ μ− ≤ − ≤
2.27. Two types of plastic are suitable for use by an electronic calculator manufacturer. The
breaking strength of this plastic is important. It is known that σ1 = σ2 = 1.0 psi. From random
samples of n1 = 10 and n2 = 12 we obtain y1 = 162.5 and y2 = 155.0. The company will not adopt
plastic 1 unless its breaking strength exceeds that of plastic 2 by at least 10 psi. Based on the sample
information, should they use plastic 1? In answering this questions, set up and test appropriate
hypotheses using α = 0.01. Construct a 99 percent confidence interval on the true mean difference in
breaking strength.
H0: μ1 - μ2 =10 H1: μ1 - μ2 >10
1
1
1
162.5
1
10
y
n
σ
=
=
=
2
2
2
155.0
1
10
y
n
σ
=
=
=
σ σ
− − − −
= = = −
++
1 2
2 2 2 2
1 2
1 2
10 162.5 155.0 10
5.84
1 1
10 12
o
y y
z
n n
z0.01 = 2.325; do not reject
The 99 percent confidence interval is
2 2
2 2 2 2
1 2 1 2
1 2 1 2 1 2
1 2 1 2
y y z y y z
n n n n
α α
σ σ σ σ
μ μ− − + ≤ − ≤ − + +
2 2 2 2
1 2
1 1 1 1
(162.5 155.0) (2.575) (162.5 155.0) (2.575)
10 12 10 12
μ μ− − + ≤ − ≤ − + +
1 26.40 8.60μ μ≤ − ≤

2-15
2.28. The following are the burning times (in minutes) of chemical flares of two different
formulations. The design engineers are interested in both the means and variance of the burning
times.
Type 1 Type 2
65 82 64 56
81 67 71 69
57 59 83 74
66 75 59 82
82 70 65 79
(a) Test the hypotheses that the two variances are equal. Use α = 0.05.
2 2
0 1 2
2 2
1 1 2
:
:
H
H
σ σ
σ σ
=
≠
Do not reject.
(b) Using the results of (a), test the hypotheses that the mean burning times are equal. Use α =
0.05. What is the P-value for this test?
Do not reject.
From the computer output, t=0.05; do not reject. Also from the computer output P=0.96
Minitab Output
Two Sample T-Test and Confidence Interval
Two sample T for Type 1 vs Type 2
N Mean StDev SE Mean
Type 1 10 70.40 9.26 2.9
Type 2 10 70.20 9.37 3.0
95% CI for mu Type 1 - mu Type 2: ( -8.6, 9.0)
T-Test mu Type 1 = mu Type 2 (vs not =): T = 0.05 P = 0.96 DF = 18
Both use Pooled StDev = 9.32
(c) Discuss the role of the normality assumption in this problem. Check the assumption of
normality for both types of flares.
The assumption of normality is required in the theoretical development of the t-test. However,
moderate departure from normality has little impact on the performance of the t-test. The normality
assumption is more important for the test on the equality of the two variances. An indication of
nonnormality would be of concern here. The normal probability plots shown below indicate that
burning time for both formulations follow the normal distribution.

2-16
2.29. An article in Solid State Technology, "Orthogonal Design of Process Optimization and Its
Application to Plasma Etching" by G.Z. Yin and D.W. Jillie (May, 1987) describes an experiment to
determine the effect of C2F6 flow rate on the uniformity of the etch on a silicon wafer used in
integrated circuit manufacturing. Data for two flow rates are as follows:
C2F6 Uniformity Observation
(SCCM) 1 2 3 4 5 6
125 2.7 4.6 2.6 3.0 3.2 3.8
200 4.6 3.4 2.9 3.5 4.1 5.1

2-17
(a) Does the C2F6 flow rate affect average etch uniformity? Use α = 0.05.
No, C2F6 flow rate does not affect average etch uniformity.
Minitab Output
Two Sample T-Test and Confidence Interval
Two sample T for Uniformity
Flow Rat N Mean StDev SE Mean
125 6 3.317 0.760 0.31
200 6 3.933 0.821 0.34
95% CI for mu (125) - mu (200): ( -1.63, 0.40)
T-Test mu (125) = mu (200) (vs not =): T = -1.35 P = 0.21 DF = 10
(b) What is the P-value for the test in part (a)? From the Minitab output, P=0.21
(c) Does the C2F6 flow rate affect the wafer-to-wafer variability in etch uniformity? Use α = 0.05.
2 2
0 1 2
2 2
1 1 2
0.025,5,5
0.975,5,5
0
:
:
7.15
0.14
0.5776
0.86
0.6724
H
H
F
F
F
σ σ
σ σ
=
≠
=
=
= =
Do not reject; C2F6 flow rate does not affect wafer-to-wafer variability.
(d) Draw box plots to assist in the interpretation of the data from this experiment.
The box plots shown below indicate that there is little difference in uniformity at the two gas flow
rates. Any observed difference is not statistically significant. See the t-test in part (a).

2-18
2.30. A new filtering device is installed in a chemical unit. Before its installation, a random sample
yielded the following information about the percentage of impurity: y1 = 12.5,
2
1S =101.17, and
n1
= 8. After installation, a random sample yielded y2 = 10.2,
2
2S = 94.73, n2
= 9.
(a) Can you conclude that the two variances are equal? Use α = 0.05.
2 2
0 1 2
2 2
1 1 2
0.025,7,8
2
1
0 2
2
:
:
4.53
101.17
1.07
94.73
H
H
F
S
F
S
σ σ
σ σ
=
≠
=
= = =
Do not reject. Assume that the variances are equal.
(b) Has the filtering device reduced the percentage of impurity significantly? Use α = 0.05.
μ μ
μ μ
=
>
− + − − + −
= = =
+ − + −
=
− −
= = =
+ +
=
0 1 2
1 1 2
2 2
2 1 1 2 2
1 2
1 2
0
1 2
0.05,15
:
:
( 1) ( 1) (8 1)(101.17) (9 1)(94.73)
97.74
2 8 9 2
9.89
12.5 10.2
0.479
1 1 1 1
9.89
8 9
1.753
p
p
p
H
H
n S n S
S
n n
S
y y
t
S
n n
t
Do not reject. There is no evidence to indicate that the new filtering device has affected the mean.
2.31. Photoresist is a light-sensitive material applied to semiconductor wafers so that the circuit
pattern can be imaged on to the wafer. After application, the coated wafers are baked to remove the
solvent in the photoresist mixture and to harden the resist. Here are measurements of photoresist
thickness (in kÅ) for eight wafers baked at two different temperatures. Assume that all of the runs
were made in random order.
95 ºC 100 ºC
11.176 5.623
7.089 6.748
8.097 7.461
11.739 7.015
11.291 8.133
10.759 7.418
6.467 3.772
8.315 8.963

2-19
(a) Is there evidence to support the claim that the higher baking temperature results in wafers with
a lower mean photoresist thickness? Use α = 0.05.
μ μ
μ μ
=
>
− + − − + −
= = =
+ − + −
=
− −
= = =
+ +
=
0 1 2
1 1 2
2 2
2 1 1 2 2
1 2
1 2
0
1 2
0.05,14
:
:
( 1) ( 1) (8 1)(4.41) (8 1)(2.54)
3.48
2 8 8 2
1.86
9.37 6.89
2.65
1 1 1 1
1.86
8 8
1.761
p
p
p
H
H
n S n S
S
n n
S
y y
t
S
n n
t
Since t0.05,14 = 1.761, reject H0. There appears to be a lower mean thickness at the higher temperature.
This is also seen in the computer output.
Minitab Output
Two-Sample T-Test and CI: Thickness, Temp
Two-sample T for Thick@95 vs Thick@100
Thick@95 8 9.37 2.10 0.74
Thick@10 8 6.89 1.60 0.56
Difference = mu Thick@95 - mu Thick@100
Estimate for difference: 2.475
95% lower bound for difference: 0.833
T-Test of difference = 0 (vs >): T-Value = 2.65 P-Value = 0.009 DF = 14
(b) What is the P-value for the test conducted in part (a)? P = 0.009
(c) Find a 95% confidence interval on the difference in means. Provide a practical interpretation
of this interval.
From the computer output the 95% lower confidence bound is μ μ≤ −1 20.833 . This lower
confidence bound is greater than 0; therefore, there is a difference in the two temperatures on the
thickness of the photoresist.

2-20
(d) Draw dot diagrams to assist in interpreting the results from this experiment.
(e) Check the assumption of normality of the photoresist thickness.

2-21
There are no significant deviations from the normality assumptions.
(f) Find the power of this test for detecting an actual difference in means of 2.5 kÅ.
Minitab Output
Power and Sample Size
2-Sample t Test
Testing mean 1 = mean 2 (versus not =)
Calculating power for mean 1 = mean 2 + difference
Alpha = 0.05 Sigma = 1.86
Sample
Difference Size Power
2.5 8 0.7056
(g) What sample size would be necessary to detect an actual difference in means of 1.5 kÅ with a
power of at least 0.9?.
Minitab Output
Power and Sample Size
2-Sample t Test
Testing mean 1 = mean 2 (versus not =)
Calculating power for mean 1 = mean 2 + difference
Alpha = 0.05 Sigma = 1.86
Sample Target Actual
Difference Size Power Power
1.5 34 0.9000 0.9060
This result makes intuitive sense. More samples are needed to detect a smaller difference.

2-22
2.32. Front housings for cell phones are manufactured in an injection molding process. The time the
part is allowed to cool in the mold before removal is thought to influence the occurrence of a
particularly troublesome cosmetic defect, flow lines, in the finished housing. After manufacturing,
the housings are inspected visually and assigned a score between 1 and 10 based on their appearance,
with 10 corresponding to a perfect part and 1 corresponding to a completely defective part. An
experiment was conducted using two cool-down times, 10 seconds and 20 seconds, and 20 housings
were evaluated at each level of cool-down time. All 40 observations in this experiment were run in
random order. The data are shown below.
10 Seconds 20 Seconds
1 3 7 6
2 6 8 9
1 5 5 5
3 3 9 7
5 2 5 4
1 1 8 6
5 6 6 8
2 8 4 5
3 2 6 8
5 3 7 7
(a) Is there evidence to support the claim that the longer cool-down time results in fewer
appearance defects? Use α = 0.05.
From the analysis shown below, there is evidence that the longer cool-down time results in fewer
appearance defects.
Minitab Output
Two-Sample T-Test and CI: 10 seconds, 20 seconds
Two-sample T for 10 seconds vs 20 seconds
10 secon 20 3.35 2.01 0.45
20 secon 20 6.50 1.54 0.34
Difference = mu 10 seconds - mu 20 seconds
Estimate for difference: -3.150
95% upper bound for difference: -2.196
T-Test of difference = 0 (vs <): T-Value = -5.57 P-Value = 0.000 DF = 38
(b) What is the P-value for the test conducted in part (a)? From the Minitab output, P = 0.000
(c) Find a 95% confidence interval on the difference in means. Provide a practical interpretation
of this interval.
From the Minitab output, 1 2 2.196μ μ− ≤ − . This lower confidence bound is less than 0. The two
samples are different. The 20 second cooling time gives a cosmetically better housing.

Solution manual for design and analysis of experiments 9th edition douglas c. montgomery

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