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==Extended uses==
==Extended uses==
These designs can be augmented with positive and negative "axial points", as in [[Central composite design]]s, but here to estimate univariate cubic and quartic effects, having length "alpha" = min(2,&nbsp;(int(1.5&nbsp;+&nbsp;''K''/4))<sup>1/2</sup>), for ''K'' factors.
These designs can be augmented with positive and negative "axial points", as in [[Central composite design]]s, but, in this case, to estimate univariate cubic and quartic effects, with length "alpha" = min(2,&nbsp;(int(1.5&nbsp;+&nbsp;''K''/4))<sup>1/2</sup>), for ''K'' factors, to approximate original design points' distance from the centre.


==See also==
==See also==
* [[Plackett–Burman_design#Extended_uses|Extending Plackett–Burman designs to construct smaller or larger Box–Behnkens]]
* [[Plackett–Burman_design#Extended_uses|Extending Plackett–Burman designs to construct smaller or larger Box–Behnkens]] in which case, axial points of length alpha = (''K''+1)/2)<sup>1/2</sup> better approximate original design points' distance from the centre.


==References==
==References==

Revision as of 18:35, 27 August 2013

In statistics, Box–Behnken designs are experimental designs for response surface methodology, devised by George E. P. Box and Donald Behnken in 1960, to achieve the following goals:

  • Each factor, or independent variable, is placed at one of three equally spaced values. (At least three levels are needed for the following goal.)
  • The design should be sufficient to fit a quadratic model, that is, one containing squared terms and products of two factors.
  • The ratio of the number of experimental points to the number of coefficients in the quadratic model should be reasonable (in fact, their designs kept it in the range of 1.5 to 2.6).
  • The estimation variance should more or less depend only on the distance from the centre (this is achieved exactly for the designs with 4 and 7 factors), and should not vary too much inside the smallest (hyper)cube containing the experimental points.

The design with 7 factors was found first while looking for a design having the desired property concerning estimation variance, and then similar designs were found for other numbers of factors.

Each design can be thought of as a combination of a two-level (full or fractional) factorial design with an incomplete block design. In each block, a certain number of factors are put through all combinations for the factorial design, while the other factors are kept at the central values. For instance, the Box–Behnken design for 3 factors involves three blocks, in each of which 2 factors are varied through the 4 possible combinations of high and low. It is necessary to include centre points as well (in which all factors are at their central values).

In this table, m represents the number of factors which are varied in each of the blocks.

factors m no. of blocks factorial pts. per block total with 1 centre point typical total with extra centre points no. of coefficients in quadratic model
3 2 3 4 13 15, 17 10
4 2 6 4 25 27, 29 15
5 2 10 4 41 46 21
6 3 6 8 49 54 28
7 3 7 8 57 62 36
8 4 14 8 113 120 45
9 3 15 8 121 130 55
10 4 10 16 161 170 66
11 5 11 16 177 188 78
12 4 12 16 193 204 91
16 4 24 16 385 396 153

The design for 8 factors was not in the original paper. Designs for other numbers of factors have also been invented (at least up to 21). A design for 16 factors exists having only 256 factorial points.

Most of these designs can be split into groups (blocks), for each of which the model will have a different constant term, in such a way that the block constants will be uncorrelated with the other coefficients.

Extended uses

These designs can be augmented with positive and negative "axial points", as in Central composite designs, but, in this case, to estimate univariate cubic and quartic effects, with length "alpha" = min(2, (int(1.5 + K/4))1/2), for K factors, to approximate original design points' distance from the centre.

See also

References