Central composite design
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In statistics, a central composite design is an experimental design, useful in response surface methodology, for building a second order (quadratic) model for the response variable without needing to use a complete three level factorial experiment.
After the designed experiment is performed, linear regression is used, sometimes iteratively, to obtain results. Coded variables are often used when constructing this design.
[edit] Design matrix
The matrix, d, for an experiment involving k factors consists of the following three different parts:
- The matrix obtained from the
factorial experiment. This will be denoted by F.
- The centre of the system of interest, denoted in coded variables as (0,0,0,...,0), where there are k zeros. This point is often repeated in order to improve the resolution of the method. This part will be denoted by C.
- A matrix, with
row, where each factor is placed at
and all other factors are at zero. The α value is determined by the designer, and it can have just about any value. This part is denoted by E and would look like this:

Thus, the d matrix will look like this:


The X matrix used in linear regression would be constructed as follows:


where d(i) represents the
column in d. The multiplication is to be done memberwise.
[edit] Determining the value of 
There are many different methods to determine the value of α. Define
, the number of points due to the factorial design and
, the number of additional points, where
is the number of central points in the design. Common values are as follows (Myers, 1971):
- Orthogonal design::
, where
;
- Rotatable design:
, which is the design implemented by MATLAB’s “ccdesign(k)” function.
[edit] Reference
Myers, Raymond H. Response Surface Methodology. Boston: Allyn and Bacon, Inc., 1971
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