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Statistical Modeling for Discrete Circuits
Examples of Factor Screening Experiments
Statistical Experimental Design and Analysis
Factorial Design Comparison with Worse Case Analysis
Factorial Design Comparison with Worse Case
Analysis
The traditional “one-factor-at-a-time” design method based on a single or double-sided
sensitivity analysis does not conduct a systematic or comprehensive search over the
experimental region.
By comparison with the factorial design method implemented in the DEX procedures,
traditional design methods have the following disadvantages:
• Some interactions cannot be estimated.
• Conclusions from the analysis are not general.
• Optimal settings of factors can be missed.
When investigating main effects, the following main effect model around the nominal point x 0
is assumed:
This model can be applied to the following cases:
• Where interactions can be tentatively assumed to be zero.
• For screening a large set of factors where some factors have large main effects on the
output y.
The traditional “one-factor-at-a-time” design method to estimate the coefficients α i is used in
the sensitivity computations of the .WCASE command. Here, each of the variables x i are
perturbed by ±Δx i about the nominal point x 0 , keeping all the others at their nominal values.
The .WCASE command also provides the so-called worst case values of the response y(x)
where x is replaced by the following ‘worst case points’:
where the coordinates are all set to their extreme values: a low level of x low = (1− Δ) · x i and a
high level of x high = (1+ Δ) · x i .
Eldo® User's Manual, 15.3 559