Chapter 4 and 5 solutions 4.4. Three different washing solutions are being compared to study their effectiveness in retarding bacteria growth in five‐gallon milk containers. The analysis is done in a laboratory, and only three trials can be run on any day. Because days could represent a potential source of variability, the experimenter decides to use a randomized block design. Observations are taken for four days, and the data are shown here. Analyze the data from this experiment (use α = 0.05) and draw conclusions. Days
Solution
1
2
3
4
1
13
22
18
39
2
16
24
17
44
3
5
4
1
22
Design Expert Output Response: Growth ANOVA for Selected Factorial Model Analysis of variance table [Partial sum of squares] Sum of Mean F Source Squares DF Square Value Prob > F Block 1106.92 3 368.97 Model 703.50 2 351.75 40.72 0.0003 A 703.50 2 351.75 40.72 0.0003 Residual 51.83 6 8.64 Cor Total 1862.25 11 The Model F‐value of 40.72 implies the model is significant. There is only a 0.03% chance that a "Model F‐Value" this large could occur due to noise. Std. Dev. 2.94 R‐Squared 0.9314 significant Mean 18.75 Adj R‐Squared 0.9085 C.V. 15.68 Pred R‐Squared 0.7255 207.33 Adeq Precision PRESS 19.687 Treatment Means (Adjusted, If Necessary) Estimated Standard Mean 1‐1 23.00 1.47 2‐2 25.25 1.47 3‐3 8.00 1.47 Error Mean Standard t for H0 Difference DF Error Coeff=0 Prob > |t| 1 vs 2 ‐2.25 1 2.08 ‐1.08 0.3206 1 vs 3 15.00 1 2.08 7.22 0.0004 2 vs 3 17.25 1 2.08 8.30 0.0002 Treatment There is a difference between the means of the three solutions. The Fisher LSD procedure indicates that solution 3 is significantly different than the other two. 4.7. Consider the hardness testing experiment described in Section 4.1. Suppose that the experiment was conducted as described and the following Rockwell C‐scale data (coded by subtracting 40 units) obtained: Coupon
Tip
1
2
3
4
1
9.3
9.4
9.6
10.0
2
9.4
9.3
9.8
9.9
3
9.2
9.4
9.5
9.7
4
9.7
9.6
10.0
10.2
(a) Analyize the data from this experiment. There is a difference between the means of the four tips. Design Expert Output Response: Hardness ANOVA for Selected Factorial Model Analysis of variance table [Terms added sequentially (first to last)] Sum of Mean F Source Squares DF Square Value Prob > F Bock 0.82 3 0.27 Model 0.38 3 0.13 14.44 0.0009 A 0.38 3 0.13 14.44 0.0009 Residual 0.080 9 8.889E‐003 Cor Total 1.29 15 The Model F‐value of 14.44 implies the model is significant. There is only a 0.09% chance that a "Model F‐Value" this large could occur due to noise. Std. Dev. 0.094 R‐Squared 0.8280 Mean 9.63 Adj R‐Squared 0.7706 C.V. 0.98 Pred R‐Squared 0.4563 PRESS 0.25 Adeq Precision Treatment Means (Adjusted, If Necessary) Estimated Mean Standard Error 1‐1 9.57 0.047 2‐2 9.60 0.047 15.635 significant 3‐3 9.45 0.047 4‐4 9.88 0.047 Mean Standard t for H0 Difference DF Error Coeff=0 Prob > |t| 1 vs 2 ‐0.025 1 0.067 ‐0.38 0.7163 1 vs 3 0.13 1 0.067 1.87 0.0935 1 vs 4 ‐0.30 1 0.067 ‐4.50 0.0015 2 vs 3 0.15 1 0.067 2.25 0.0510 2 vs 4 ‐0.27 1 0.067 ‐4.12 0.0026 3 vs 4 ‐0.43 1 0.067 ‐6.37 0.0001 Treatment (b) Use the Fisher LSD method to make comparisons among the four tips to determine specifically which tips differ in mean hardness readings. Based on the LSD bars in the Design Expert plot below, the mean of tip 4 differs from the means of tips 1, 2, and 3. The LSD method identifies a marginal difference between the means of tips 2 and 3. One Factor Plot
10.2
Hardness
9.95
9.7
9.45
9.2
1
2
3
4
A: Tip
(c) Analyze the residuals from this experiment. The residual plots below do not identify any violations to the assumptions. Normal Plot of Residuals
Residuals vs. Predicted
0.15
99
95
80
Residuals
Normal % Probability
0.0875
90
70
50
30
0.025
2
20
10
-0.0375
5
1
2
-0.1
-0.1
-0.0375
0.025
0.0875
9.22
0.15
9.47
9.71
9.96
10.20
Predicted
Residual
Residuals vs. Tip
0.15
Residuals
0.0875
0.025
-0.0375
-0.1
1
2
3
4
Tip
4.8. A consumer products company relies on direct mail marketing pieces as a major component of its advertising campaigns. The company has three different designs for a new brochure and want to evaluate their effectiveness, as there are substantial differences in costs between the three designs. The company decides to test the three designs by mailing 5,000 samples of each to potential customers in four different regions of the country. Since there are known regional differences in the customer base, regions are considered as blocks. The number of responses to each mailing is shown below. Region
Design
NE
NW
SE
SW
1 250
350
219
375
2 400
525
390
580
3 275
340
200
310
(a) Analyze the data from this experiment. The residuals of the analsysis below identify concerns with the normality and equality of variance assumptions. As a result, a square root transformation was applied as shown in the second ANOVA table. The residuals of both analysis are presented for comparison in part (c) of this problem. The analysis concludes that there is a difference between the mean number of responses for the three designs. Design Expert Output Response: Number of responses ANOVA for Selected Factorial Model Analysis of variance table [Terms added sequentially (first to last)] Sum of Mean F Source Squares DF Square Value Prob > F Block 49035.67 3 16345.22 Model 90755.17 2 45377.58 50.15 0.0002 A 90755.17 2 45377.58 50.15 0.0002 Residual 5428.83 6 904.81 Cor Total 1.452E+005 11 The Model F‐value of 50.15 implies the model is significant. There is only a 0.02% chance that a "Model F‐Value" this large could occur due to noise. significant Std. Dev. Mean 30.08 R‐Squared 0.9436 351.17 Adj R‐Squared 0.9247 8.57 Pred R‐Squared 0.7742 21715.33 Adeq Precision C.V. PRESS 16.197 Treatment Means (Adjusted, If Necessary) Estimated Mean Standard Error 1‐1 298.50 15.04 2‐2 473.75 15.04 3‐3 281.25 15.04 Mean Standard t for H0 Difference DF Error Coeff=0 Prob > |t| 1 vs 2 ‐175.25 1 21.27 ‐8.24 0.0002 1 vs 3 17.25 1 21.27 0.81 0.4483 2 vs 3 192.50 1 21.27 9.05 0.0001 Treatment Design Expert Output for Transformed Data Response: Number of responsesTransform:Square root Constant: 0 ANOVA for Selected Factorial Model Analysis of variance table [Terms added sequentially (first to last)] Sum of Mean F Source Squares DF Square Value Prob > F Block 35.89 3 11.96 Model 60.73 2 30.37 60.47 0.0001 A 60.73 2 30.37 60.47 0.0001 Residual 3.01 6 0.50 Cor Total 99.64 11 significant The Model F‐value of 60.47 implies the model is significant. There is only a 0.01% chance that a "Model F‐Value" this large could occur due to noise. Std. Dev. Mean 0.71 R‐Squared 0.9527 18.52 Adj R‐Squared 0.9370 3.83 Pred R‐Squared 0.8109 12.05 Adeq Precision C.V. PRESS 18.191 Treatment Means (Adjusted, If Necessary) Estimated Standard Mean 1‐1 17.17 0.35 2‐2 21.69 0.35 3‐3 16.69 0.35 Error Mean Standard t for H0 Difference DF Error Coeff=0 Prob > |t| 1 vs 2 ‐4.52 1 0.50 ‐9.01 0.0001 1 vs 3 0.48 1 0.50 0.95 0.3769 2 vs 3 4.99 1 0.50 9.96 < 0.0001 Treatment (b) Use the Fisher LSD method to make comparisons among the three designs to determine specifically which designs differ in mean response rate. Based on the LSD bars in the Design Expert plot below, designs 1 and 3 do not differ; however, design 2 is different than designs 1 and 3. One Factor Plot
Sqrt(Number of responses)
24.083
21.598
19.113
16.627
14.142
1
2
3
A: Design
(c) Analyze the residuals from this experiment. The first set of residual plots presented below represent the untransformed data. Concerns with normality as well as inequality of variance are presented. The second set of residual plots represent transformed data and do not identify significant violations of the assumptions. The residuals vs. design plot indicates a slight inequality of variance; however, not a strong violation and an improvement over the non‐transformed data. Normal Plot of Residuals
Residuals vs. Predicted
36.5833
99
17
90
80
70
Residuals
Normal % Probability
95
50
30
-2.58333
20
10
-22.1667
5
1
-41.75
-41.75
-22.1667
-2.58333
Residual
17
36.5833
199.75
285.88
372.00
458.13
544.25
Predicted
Residuals vs. Design
36.5833
Residuals
17
-2.58333
-22.1667
-41.75
1
2
3
Design
The following are the square root transformed data residual plots. Normal Plot of Residuals
Residuals vs. Predicted
0.942069
99
95
80
Residuals
Normal % Probability
0.476292
90
70
50
30
0.0105142
20
10
-0.455263
5
1
-0.921041
-0.921041
-0.455263
0.0105142
Residual
0.476292
0.942069
14.41
16.68
18.96
21.24
23.52
Predicted
Residuals vs. Design
0.942069
Residuals
0.476292
0.0105142
-0.455263
-0.921041
1
2
3
Design
4.12. An aluminum master alloy manufacturer produces grain refiners in ingot form. The company produces the product in four furnaces. Each furnace is known to have its own unique operating characteristics, so any experiment run in the foundry that involves more than one furnace will consider furnaces as a nuisance variable. The process engineers suspect that stirring rate impacts the grain size of the product. Each furnace can be run at four different stirring rates. A randomized block design is run for a particular refiner and the resulting grain size data is as follows. Furnace
Stirring Rate
1
2
3
4
5
8
4
5
6
10
14
5
6
9
15
14
6
9
2
20
17
9
3
6
(a) Is there any evidence that stirring rate impacts grain size? Design Expert Output Response: Grain Size ANOVA for Selected Factorial Model Analysis of variance table [Partial sum of squares] Sum of Mean F Source Squares DF Square Value Prob > F Block 165.19 3 55.06 Model 22.19 3 7.40 0.85 0.4995 A 22.19 3 7.40 0.85 0.4995 Residual 78.06 9 8.67 Cor Total 265.44 15 not significant The "Model F‐value" of 0.85 implies the model is not significant relative to the noise. There is a 49.95 % chance that a "Model F‐value" this large could occur due to noise. Std. Dev. 2.95 R‐Squared 0.2213 Mean 7.69 Adj R‐Squared ‐0.0382 38.31 Pred R‐Squared ‐1.4610 246.72 Adeq Precision 5.390 C.V. PRESS Treatment Means (Adjusted, If Necessary) Estimated Mean Standard Error 1‐5 5.75 1.47 2‐10 8.50 1.47 3‐15 7.75 1.47 4‐20 8.75 1.47 Mean Standard t for H0 Difference DF Error Coeff=0 Prob > |t| 1 vs 2 ‐2.75 1 2.08 ‐1.32 0.2193 1 vs 3 ‐2.00 1 2.08 ‐0.96 0.3620 1 vs 4 ‐3.00 1 2.08 ‐1.44 0.1836 Treatment 2 vs 3 0.75 1 2.08 0.36 0.7270 2 vs 4 ‐0.25 1 2.08 ‐0.12 0.9071 3 vs 4 ‐1.00 1 2.08 ‐0.48 0.6425 The analysis of variance shown above indicates that there is no difference in mean grain size due to the different stirring rates. (b) Graph the residuals from this experiment on a normal probability plot. Interpret this plot. Normal plot of residuals
99
Norm al % probability
95
90
80
70
50
30
20
10
5
1
-3.8125
-2.0625
-0.3125
1.4375
3.1875
Res idual
The plot indicates that normality assumption is valid. (c) Plot the residuals versus furnace and stirring rate. Does this plot convey any useful information? Residuals vs. Furnace
Residuals vs. Stirring Rate
3.1875
1.4375
1.4375
Residuals
Res iduals
3.1875
-0.3125
2
-0.3125
-2.0625
-2.0625
-3.8125
-3.8125
1
2
3
4
1
2
Stirring Rate
3
4
Furnace
The variance is consistent at different stirring rates. Not only does this validate the assumption of uniform variance,
it also identifies that the different stirring rates do not affect variance.
(d) What should the process engineers recommend concerning the choice of stirring rate and furnace for this particular grain refiner if small grain size is desirable? There really is no effect due to the stirring rate. 4.20. The effect of five different ingredients (A, B, C, D, E) on reaction time of a chemical process is being studied. Each batch of new material is only large enough to permit five runs to be made. Furthermore, each run requires approximately 1 1/2 hours, so only five runs can be made in one day. The experimenter decides to run the experiment as a Latin square so that day and batch effects can be systematically controlled. She obtains the data that follow. Analyze the data from this experiment (use α = 0.05) and draw conclusions. Day
Batch 1
2
3
4
5
1 A=8
B=7
D=1
C=7
E=3
2 C=11
E=2
A=7
D=3
B=8
3 B=4
A=9
C=10
E=1
D=5
4 D=6
C=8
E=6
B=6
A=10
5 E=4
D=2
B=3
A=8
C=8
The Minitab output below identifies the ingredients as having a significant effect on reaction time. Minitab Output General Linear Model
Factor
Type Levels Values
Batch
random
5 1 2 3 4 5
Day
random
5 1 2 3 4 5
fixed
5 A B C D E
Catalyst
Analysis of Variance for Time, using Adjusted SS for Tests
Source
DF
Seq SS
Adj SS
Adj MS
F
P
Catalyst
4
141.440
141.440
35.360
11.31
0.000
Batch
4
15.440
15.440
3.860
1.23
0.348
Day
4
12.240
12.240
3.060
0.98
0.455
Error
12
37.520
37.520
3.127
Total
24
206.640
5.3. The yield of a chemical process is being studied. The two most important variables are thought to be the pressure and the temperature. Three levels of each factor are selected, and a factorial experiment with two replicates is performed. The yield data follow: Pressure
Temperature
200
215
230
150
90.4
90.7
90.2
160
170
90.2
90.6
90.4
90.1
90.5
89.9
90.3
90.6
90.1
90.5
90.8
90.4
90.7
90.9
90.1
(a) Analyze the data and draw conclusions. Use α = 0.05. Both pressure (A) and temperature (B) are significant, the interaction is not. Design Expert Output
Response:Surface Finish ANOVA for Selected Factorial Model Analysis of variance table [Partial sum of squares] Sum of Mean F Source Squares DF Square Value Prob > F Model 1.14 8 0.14 8.00 0.0026 A 0.77 2 0.38 21.59 0.0004 B 0.30 2 0.15 8.47 0.0085 AB 0.069 4 0.017 0.97 0.4700 Residual 0.16 9 0.018 Lack of Fit 0.000 0 Pure Error 0.16 9 Cor Total 1.30 17 0.018 The Model F‐value of 8.00 implies the model is significant. There is only a 0.26% chance that a "Model F‐Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B are significant model terms. significant Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model. (b) Prepare appropriate residual plots and comment on the model’s adequacy. The residual plots show no serious deviations from the assumptions. Residuals vs. Predicted
Normal plot of residuals
0.15
99
95
Res iduals
Norm al % probability
0.075
4.26326E-014
-0.075
90
80
70
50
30
20
10
5
1
-0.15
90.00
90.21
90.43
90.64
90.85
-0.15
Predicted
-0.075
-4.26326E-014
0.075
0.15
Res idual
Residuals vs. Temperature
Residuals vs. Pressure
0.15
0.15
2
2
0.075
0.075
Res iduals
Res iduals
3
4.26326E-014
4.26326E-014
-0.075
-0.075
2
2
-0.15
2
-0.15
1
2
Tem perature
3
1
2
3
Pres sure
(c) Under what conditions would you operate this process? DESIGN-EXPERT Plot
Yield
Interaction Graph
Tem perature
91.0008
X = A: Pressure
Y = B: Temperature
Design Points
90.7129
2
Yie ld
B1 150
B2 160
B3 170
90.425
2
90.1371
2
89.8492
200
215
230
Pres s ure
Set pressure at 215 and Temperature at the high level, 170 degrees C, as this gives the highest yield. The standard analysis of variance treats all design factors as if they were qualitative. In this case, both factors are quantitative, so some further analysis can be performed. In Section 5.5, we show how response curves and surfaces can be fit to the data from a factorial experiment with at least one quantative factor. Since both factors in this problem are quantitative and have three levels, we can fit linear and quadratic effects of both temperature and pressure, exactly as in Example 5.5 in the text. The Design‐Expert output, including the response surface plots, now follows. Design Expert Output
Response:Surface Finish ANOVA for Selected Factorial Model Analysis of variance table [Partial sum of squares] Sum of Mean F Source Squares DF Square Value Prob > F Model 1.13 5 0.23 16.18 < 0.0001 A 0.10 1 0.10 7.22 0.0198 B 0.067 1 0.067 4.83 0.0483 significant A2 0.67 1 0.67 47.74 < 0.0001 B2 0.23 1 0.23 16.72 0.0015 AB 0.061 1 0.061 4.38 0.0582 Residual 0.17 12 0.014 Lack of Fit Pure Error Cor Total 1.30 7.639E‐003 3 0.16 0.018 9 2.546E‐003 0.14 0.9314 not significant 17 The Model F‐value of 16.18 implies the model is significant. There is only a 0.01% chance that a "Model F‐Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B, A2, B2 are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model. Std. Dev.
Mean
C.V.
0.12
90.41
0.13
R-Squared
Adj R-Squared
Pred R-Squared
PRESS 0.42 Adeq Precision 0.8708
0.8170
0.6794
11.968 Coefficient Standard 95% CI 95% CI Factor Estimate DF Error Low High 90.52 1 0.062 90.39 90.66 ‐0.092 1 0.034 ‐0.17 ‐0.017 1.00 B‐Temperature 0.075 1 0.034 6.594E‐004 0.15 1.00 A2 ‐0.41 1 0.059 ‐0.54 ‐0.28 1.00 B2 0.24 1 0.059 0.11 0.37 1.00 AB ‐0.087 1 0.042 ‐0.18 3.548E‐003 1.00 Intercept A‐Pressure VIF Final Equation in Terms of Coded Factors: Yield = +90.52 ‐0.092 * A +0.075 * B ‐0.41 * A2 +0.24 * B2 ‐0.087 * A * B Final Equation in Terms of Actual Factors: Yield = +48.54630 +0.86759 * Pressure ‐0.64042 * Temperature ‐1.81481E‐003 * Pressure2 +2.41667E‐003 * Temperature2 ‐5.83333E‐004 * Pressure * Temperature Yield
2
2
1 7 0 .0 0
2
90.8
90.7
90.6
91
90.8
90.2
90.5
2
1 6 0 .0 0
90.5
2
90.4 90.3
90.6
90.12
90.4
90.4
Yield
B: Tem perature
1 6 5 .0 0
90.3
90.2
90
1 5 5 .0 0
170.00
90.6
2
2
200.00
2
207.50
1 5 0 .0 0
2 0 0 .0 0
2 0 7 .5 0
2 1 5 .0 0
2 2 2 .5 0
165.00
2 3 0 .0 0
160.00
215.00
B: Tem perature
155.00
222.50
A: Pres s ure
A: Pre s s ure
230.00
150.00
5.4. An engineer suspects that the surface finish of a metal part is influenced by the feed rate and the depth of cut. She selects three feed rates and four depths of cut. She then conducts a factorial experiment and obtains the following data: Depth of
Cut (in)
Feed Rate (in/min)
0.15
0.18
0.20
0.25
74
79
82
99
0.20 64
68
88
104
60
73
92
96
92
98
99
104
0.25 86
104
108
110
88
88
95
99
99
104
108
114
0.30 98
99
110
111
102
95
99
107
(a) Analyze the data and draw conclusions. Use α = 0.05. The depth (A) and feed rate (B) are significant, as is the interaction (AB). Design Expert Output
Response: Surface Finish ANOVA for Selected Factorial Model Analysis of variance table [Partial sum of squares] Sum of Mean F Source Squares DF Square Value Prob > F Model 5842.67 11 531.15 18.49 < 0.0001 A 2125.11 3 708.37 24.66 < 0.0001 B 3160.50 2 1580.25 55.02 < 0.0001 AB 557.06 6 92.84 3.23 0.0180 Residual 689.33 24 28.72 Lack of Fit 0.000 0 Pure Error 689.33 24 Cor Total 6532.00 35 28.72 The Model F‐value of 18.49 implies the model is significant. There is only a 0.01% chance that a "Model F‐Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B, AB are significant model terms. (b) Prepare appropriate residual plots and comment on the model’s adequacy. significant The residual plots shown indicate nothing unusual. Residuals vs. Predicted
Normal plot of residuals
8
99
95
Res iduals
Norm al % probability
3.83333
-0.333333
-4.5
90
80
70
50
30
20
10
5
1
-8.66667
66.00
77.17
88.33
99.50
110.67
-8.66667
-4.5
-0.333333
Predicted
3.83333
8
Res idual
Residuals vs. Feed Rate
Residuals vs. Depth of Cut
8
8
2
3.83333
3.83333
Res iduals
Res iduals
2
-0.333333
-0.333333
-4.5
-4.5
-8.66667
-8.66667
1
2
3
1
Feed Rate
2
3
4
Depth of Cut
(c) Obtain point estimates of the mean surface finish at each feed rate. Feed Rate
Average
0.20
81.58
0.25
97.58
0.30
103.83
DESIGN-EXPERT Plot
One Factor Plot
Surface Finish
114
Warning! Factor involved in an interaction.
X = B: Feed Rate
S u r fa c e F i n i s h
Actual Factor
A: Depth of Cut = Average
100.5
87
73.5
60
0.20
0.25
0.30
Feed Rate
(d) Find P‐values for the tests in part (a). The P‐values are given in the computer output in part (a). 5.5. For the data in Problem 5.3, compute a 95 percent interval estimate of the mean difference in response for feed rates of 0.20 and 0.25 in/min. We wish to find a confidence interval on μ1 − μ2 , where μ1 is the mean surface finish for 0.20 in/min and μ2 is the mean surface finish for 0.25 in/min. y1.. − y 2.. − tα
2, ab (n −1)
2 MS E
≤ μ 1 − μ 2 ≤ y1.. − y 2.. + tα
n
(81.5833 − 97.5833) ± (2.064)
2, ab (n −1))
2 MS E
n
2(28.7222)
= −16 ± 9.032 3
Therefore, the 95% confidence interval for μ1 − μ2 is ‐16.000 ± 9.032. 5.6. An article in Industrial Quality Control (1956, pp. 5‐8) describes an experiment to investigate the effect of the type of glass and the type of phosphor on the brightness of a television tube. The response variable is the current necessary (in microamps) to obtain a specified brightness level. The data are as follows: Glass
Phosphor Type
Type 1
2
3
280
300
290
1 290
310
285
285
295
290
230
260
220
2 235
240
225
240
235
230
(a) Is there any indication that either factor influences brightness? Use α = 0.05. Both factors, phosphor type (A) and Glass type (B) influence brightness. Design Expert Output
Response: Current in microamps ANOVA for Selected Factorial Model Analysis of variance table [Partial sum of squares] Sum of Mean F Source Squares DF Square Value Prob > F Model 15516.67 5 3103.33 58.80 < 0.0001 A 933.33 2 466.67 8.84 0.0044 B 14450.00 1 14450.00 273.79 < 0.0001 significant AB 133.33 2 66.67 Residual 633.33 12 52.78 Lack of Fit Pure Error Cor Total 0.000 1.26 0.3178 0 633.33 12 16150.00 17 52.78 The Model F‐value of 58.80 implies the model is significant. There is only a 0.01% chance that a "Model F‐Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B are significant model terms. (b) Do the two factors interact? Use α = 0.05. There is no interaction effect. (c) Analyze the residuals from this experiment. The residual plot of residuals versus phosphor content indicates a very slight inequality of variance. It is not serious enough to be of concern, however. Normal plot of residuals
Residuals vs. Predicted
15
99
95
2.5
Norm al % probability
Res iduals
8.75
2
-3.75
90
80
70
50
30
20
10
5
1
-10
225.00
244.17
263.33
282.50
-10
301.67
-3.75
Predicted
2.5
8.75
15
Res idual
Residuals vs. Phosphor Type
15
8.75
8.75
2.5
Res iduals
Res iduals
Residuals vs. Glass Type
15
2
2
-3.75
2.5
2
2
-3.75
2
-10
-10
1
2
1
2
Glas s Type
3
Phos phor Type
5.7. Johnson and Leone (Statistics and Experimental Design in Engineering and the Physical Sciences, Wiley 1977) describe an experiment to investigate the warping of copper plates. The two factors studied were the temperature and the copper content of the plates. The response variable was a measure of the amount of warping. The data were as follows: Temperature (°C)
40
Copper
Content (%)
60
80
100 50 17,20
16,21
24,22
28,27 75 12,9
18,13
17,12
27,31 100 16,12
18,21
25,23
30,23 125 21,17
23,21
23,22
29,31 (a) Is there any indication that either factor affects the amount of warping? Is there any interaction between the factors? Use α = 0.05. Both factors, copper content (A) and temperature (B) affect warping, the interaction does not. Design Expert Output
Response: Warping ANOVA for Selected Factorial Model Analysis of variance table [Partial sum of squares] Sum of Mean F Source Squares DF Square Value Prob > F Model 968.22 15 64.55 9.52 A 698.34 3 232.78 34.33 < 0.0001 B 156.09 3 52.03 7.67 0.0021 AB 113.78 9 12.64 1.86 0.1327 Residual 108.50 16 6.78 Lack of Fit Pure Error 108.50 16 Cor Total 1076.72 31 0.000 0 6.78 The Model F‐value of 9.52 implies the model is significant. There is only a 0.01% chance that a "Model F‐Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B are significant model terms. < 0.0001 significant (b) Analyze the residuals from this experiment. There is nothing unusual about the residual plots. Residuals vs. Predicted
Normal plot of residuals
3.5
99
1.75
90
80
70
Res iduals
Norm al % probability
95
1.06581E-014
50
30
20
10
-1.75
5
1
-3.5
-3.5
-1.75
-1.06581E-014
1.75
10.50
3.5
15.38
20.25
25.13
30.00
Predicted
Res idual
Residuals vs. Copper Content
Residuals vs. Temperature
3.5
1.75
3.5
1.75
2
Res iduals
Res iduals
2
1.06581E-014
1.06581E-014
2
-1.75
2
-1.75
-3.5
-3.5
1
2
3
Copper Content
4
1
2
3
4
Tem perature
(c) Plot the average warping at each level of copper content and compare them to an appropriately scaled t distribution. Describe the differences in the effects of the different levels of copper content on warping. If low warping is desirable, what level of copper content would you specify? Design Expert Output Factor Name Level Low Level High Level A Copper Content 40 40 100 B Temperature Average 50 125 Prediction SE Mean 95% CI low 95% CI high SE Pred 95% PI low 95% PI high 0.92 13.55 17.45 2.76 9.64 21.36 Warping 15.5 Factor Name Level Low Level High Level A Copper Content 60 40 100 B Temperature Average 50 125 Prediction SE Mean 95% CI low 95% CI high SE Pred 95% PI low 95% PI high 0.92 16.92 20.83 2.76 13.02 24.73 Warping 18.875 Factor Name Level Low Level High Level A Copper Content 80 40 100 B Temperature Average 50 125 Prediction SE Mean 95% CI low 95% CI high SE Pred 95% PI low 95% PI high 0.92 19.05 22.95 2.76 15.14 26.86 SE Pred 95% PI low 95% PI high Warping 21 Factor Name Level Low Level High Level A Copper Content 100 40 100 B Temperature Average 50 125 Prediction SE Mean 95% CI low 95% CI high Warping 28.25 0.92 26.30 30.20 2.76 22.39 34.11 Use a copper content of 40 for the lowest warping. S=
MS E
6.78125
=
= 0.92 b
8
S c a le d t D is tr ib u tio n
C u=40
1 5 .0
C u=60
1 8 .0
C u=80
2 1 .0
W a r p in g
2 4 .0
C u=100
2 7 .0
(d) Suppose that temperature cannot be easily controlled in the environment in which the copper plates are to be used. Does this change your answer for part (c)? Use a copper of content of 40. This is the same as for part (c). DESIGN-EXPERT Plot
Warping
Interaction Graph
Tem perature
32.7602
2
X = A: Copper Content
Y = B: Temperature
B1
B2
B3
B4
50
75
100
125
2
26.5051
2
2
W a rp in g
Design Points
3
20.25
2
2
13.9949
2
7.73979
40
60
80
100
Copper Content
5.9. A mechanical engineer is studying the thrust force developed by a drill press. He suspects that the drilling speed and the feed rate of the material are the most important factors. He selects four feed rates and uses a high and low drill speed chosen to represent the extreme operating conditions. He obtains the following results. Analyze the data and draw conclusions. Use α = 0.05. (A) Feed
Rate (B) Drill Speed
0.015
0.030
0.045
0.060
125 2.70
2.45
2.60
2.75
2.78
2.49
2.72
2.86
200 2.83
2.85
2.86
2.94
2.86
2.80
2.87
2.88
Design Expert Output Response: Force ANOVA for Selected Factorial Model Analysis of variance table [Partial sum of squares] Sum of Mean F Source Squares DF Square Value Prob > F Model 0.28 7 0.040 15.53 0.0005 A 0.15 1 0.15 57.01 < 0.0001 B 0.092 3 0.031 11.86 0.0026 AB 0.042 3 0.014 5.37 0.0256 Residual 0.021 8 2.600E‐003 Lack of Fit 0.000 0 Pure Error 0.021 8 Cor Total 0.30 15 2.600E‐003 The Model F‐value of 15.53 implies the model is significant. There is only a 0.05% chance that a "Model F‐Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B, AB are significant model terms. The factors speed and feed rate, as well as the interaction is important. significant DESIGN-EXPERT Plot
Force
Interaction Graph
Drill Speed
2.96879
X = B: Feed Rate
Y = A: Drill Speed
Design Points
F o rc e
A1 125
A2 200
2.8294
2.69
2.5506
2.41121
0.015
0.030
0.045
0.060
Feed Rate
The standard analysis of variance treats all design factors as if they were qualitative. In this case, both factors are quantitative, so some further analysis can be performed. In Section 5.5, we show how response curves and surfaces can be fit to the data from a factorial experiment with at least one quantative factor. Since both factors in this problem are quantitative, we can fit polynomial effects of both speed and feed rate, exactly as in Example 5.5 in the text. The Design‐Expert output with only the significant terms retained, including the response surface plots, now follows. Design Expert Output
Response: Force ANOVA for Selected Factorial Model Analysis of variance table [Partial sum of squares] Sum of Mean F Source Squares DF Square Value Prob > F Model 0.23 3 0.075 11.49 0.0008 A 0.15 1 0.15 22.70 0.0005 B 0.019 1 0.019 2.94 0.1119 B2 0.058 1 0.058 8.82 0.0117 Residual 0.078 12 Lack of Fit 0.058 4 0.014 5.53 0.0196 Pure Error 0.021 8 2.600E‐003 Cor Total 0.30 15 significant 6.530E‐003 significant The Model F‐value of 11.49 implies the model is significant. There is only a 0.08% chance that a "Model F‐Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B2 are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model. Std. Dev.
Mean
C.V.
PRESS 0.081
2.77
2.92
R-Squared
Adj R-Squared
Pred R-Squared
0.14 Adeq Precision 0.7417
0.6772
0.5517
9.269 Coefficient Standard 95% CI 95% CI Factor Estimate DF Error Low High 2.69 1 0.032 2.62 2.76 A‐Drill Speed 0.096 1 0.020 0.052 0.14 1.00 B‐Feed Rate 0.047 1 0.027 ‐0.013 0.11 1.00 0.13 1 0.045 0.036 0.23 1.00 Intercept B2 Final Equation in Terms of Coded Factors: Force = +2.69 +0.096 * A +0.047 * B 2
+0.13 * B Final Equation in Terms of Actual Factors: VIF Force = +2.48917 +3.06667E‐003 * Drill Speed ‐15.76667 * Feed Rate +266.66667 * Feed Rate 2
Force
0.06 2
2
2.9
2.85
2.8
3
0.05
2.9
2
2.8
2.75
2.7
2.7
0.04
2.6
Force
B: Feed Rate
2
2.65
2
2.6
2.5
2
0.03
0.06
2.8
2.85
0.02 2
125.00
200.00
0.05
181.25
2
143.75
162.50
181.25
0.04
B: Feed Rate
A: Drill Speed
162.50
200.00
0.03
143.75
A: Drill Speed
0.02 125.00