| Reasons
designs don't work. |
Most experimental situations
call for standard designs that can be constructed in many statistical software
packages. Standard designs have assured degrees of precision, orthogonality,
and other optimal properties that are important for the exploratory nature
of most experiments. In some situations, however, standard designs are
not appropriate or are impractical. These may include situations where
-
The required blocking structure or blocking size of the experimental situation
does not fit into a standard blocked design
-
Not all combinations of the factor settings are feasible, or for some other
reason the region of experimentation is constrained or irregularly shaped.
-
A classical design needs to be 'repaired'. This can happen due to improper
planning where the original design treatment combinations contained forbidden
or unreachable combinations that were not considered before the design
was generated.
-
There is a nonlinear model.
-
A quadratic or response surface design is required in the presence of qualitative
factors.
-
The factors in the experiment include both components of a mixture and
other process variables.
-
There are multiple sources of variation leading to nested or hierarchical
data structures and restrictions on what can be randomized.
-
A standard fractional factorial design requires too many treatment combinations
for the given amount of time and / or resources,
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