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{{Reference Example|{{Banner DOE Reference Examples}}}}
{{Reference Example|{{Banner DOE Reference Examples}}}}
This example validates the robust design inner array with a two level factorial design and an out array with a general full factorial design in DOE++.
This example validates the robust design inner array with a two level factorial design and an out array with a general full factorial design in Weibull++.


{{Reference_Example_Heading1}}
{{Reference_Example_Heading1}}
The data is from the example on page 1252 in the book ''Applied linear Statistical Models'' by Michael H.K, Christopher J.N, John N. William. Li, Fifth Edition, McGraw-Hill, 2005.
The data are from the example on page 1252 in the book ''Applied linear Statistical Models'' by Michael H.K, Christopher J.N, John N. William. Li, Fifth Edition, McGraw-Hill, 2005.


{{Reference_Example_Heading2}}
{{Reference_Example_Heading2}}

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Robust Design

This example validates the robust design inner array with a two level factorial design and an out array with a general full factorial design in Weibull++.

Reference Case

The data are from the example on page 1252 in the book Applied linear Statistical Models by Michael H.K, Christopher J.N, John N. William. Li, Fifth Edition, McGraw-Hill, 2005.

Data

Run A B C Noise Factor
60°F 70°F 80°F 90°F
1 1 1 1 48 49 56 63
2 1 -1 -1 50 52 55 63
3 -1 1 1 70 72 77 84
4 -1 -1 -1 42 65 70 73
5 -1 1 -1 61 70 78 79
6 -1 -1 1 65 74 74 77
7 1 -1 1 40 59 63 66
8 1 1 -1 48 51 55 60

.

Result

From the book the results are:

Predictor Coef
Constant 62.781
A -7.906
B 1.031
C 2.031
AB -2.156
AC -1.406
BC -0.969
ABC 0.594


Results in DOE++

The software results match the book results. The ANOVA table is:

Robust design anova.png

The final equation in terms of the actual factors is:

Robust design equation.png
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