R-DOE Design: Difference between revisions

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From the book, the MLE and p values for the lognormal regression model:
 
{| {{Table}}
 
!Effect
!MLE
!P value
|-
| Intercept ||2.939||0.00
|-
| A||0.117||0.06
|-
| B||-0.201||0.00
|-
| AB||-0.049||0.43
|-
| C||-0.051||0.41
|-
| D||0.273||0.00
|-
| E||-0.153||0.02
|-
|}
 


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Revision as of 18:30, 14 July 2015

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R-DOE Design

This example validates the calculation of the reliability DOE design.

Reference Case

Data is from an example on page 530 and page 544 in the book Experiments, planning, analysis, and parameter design optimization by C.F. Jeff Wu, Michael Hamad, John Wiley & Sons, Inc. 2000.

Data Data

Run Factor Failure Time
A B C D E
1 - - - - - (14, 16) (20, [math]\displaystyle{ \infty\,\! }[/math])
2 - - + + + (18,20) (20, [math]\displaystyle{ \infty\,\! }[/math])
3 - + - - + (8, 10) (10, 12)
4 - + + + - (18, 20) (20, [math]\displaystyle{ \infty\,\! }[/math])
5 + - + - + (20, [math]\displaystyle{ \infty\,\! }[/math]) (20, [math]\displaystyle{ \infty\,\! }[/math])
6 + - + - + (12, 14) (20, [math]\displaystyle{ \infty\,\! }[/math])
7 + + - + + (16, 18) (20, [math]\displaystyle{ \infty\,\! }[/math])
8 + + + - - (12, 14) (14, 16)


Result From the book, the MLE and p values for the lognormal regression model:

Effect MLE P value
Intercept 2.939 0.00
A 0.117 0.06
B -0.201 0.00
AB -0.049 0.43
C -0.051 0.41
D 0.273 0.00
E -0.153 0.02


Results in DOE++

Your text here.

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