1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

115.6000
178.6900
58.5980
81.5360
82.1160
84.3430
58.5940
64.6080
178.7700
64.5610
111.1400
178.0600
174.3900
110.5400
64.4420
176.0000
82.6570
116.8800
110.4300
58.6120
116.6300
175.6700
83.9120
83.5590

23.0790
35.7390
11.8490
16.4930
16.2780
16.8500
11.6380
12.9390
35.7140
13.0080
22.2280
35.2110
34.8060
22.0900
12.7420
35.1840
16.3820
23.3500
22.0730
11.8420
23.2860
35.1070
16.8820
16.6640

OXIDE
THICKNESS
X1

CAP-100
Y1

CAP-250
Y2

CAP-1250
Y3

CAP-5000

2500
1620
4887
3497
3472
3420
4880
4469
1624
4471
2611
1625
1640
2613
4472
1636
3486
2470
2610
4878
2473
1641
3424
3432

9.2880
14.3560
4.7830
6.4890
6.6840
6.8550
4.7730
5.2200
14.3720
5.2550
9.0290
14.3020
13.9830
8.8890
5.2440
14.1800
6.6570
9.4120
8.7850
4.7660
9.4520
14.1270
6.8440
6.2570

449.0700
691.9000
232.2700
264.6800
319.2200
327.9800
232.1900
256.0600
692.3900
255.8400
431.9700
688.0700
675.6900
430.0300
253.8700
681.8100
321.5200
454.5500
429.3700
232.3700
452.8400
680.8900
323.3900
324.8200

OpenCAPAC vs TOX.MTW—fromStat ClassFloppy

MTB > Regressc3

1

c1

MTB > Gplot c3

c1

MTB > namec10

‘1/tox’

MTB > let c10 = 1/c1

MTB > Regressc3

1

c10

MTB > Gplot c3

c10

MTB > Corr c3

c10

c1

IMAGE section3291.gif
IMAGE section3292.gif
IMAGE section3293.gif
IMAGE section3294.gif

MultipleInputVariables:
1.ExperimentalDesign(Screening)
A. Full factorial
B. Fractional factorial
C. Factorial w/centerpoint
2.Response SurfaceModeling(Visualization)
A. Box-BehnkenDesigns
B. Central Composite Designs
C. Multiple linear regression
D. Stepwise regression
E. ContourPlots
F.Mesh Plots
3.PredictingResponseDistribution
A.Monte Carlosimulation
B. Generationof System Moments
4.PrioritizingInputVariables
5.Optimization
A.Yield Surface ModelingTM

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