Central composite designs orthogonality

Figure 13.4 Orthogonal rotatable central composite design. Square points 2, star points 2 2,...

Table 2.33 Position of the axial points for rotatability and orthogonality for central composite designs with varying number of replicates in the centre.

It is identical to the 2-factor central composite design. The matrix is "almost orthogonal" if no extra centre-points are added. It is not rotatable, nor is its precision uniform in the experimental domain. 

A fractional factorial design plus two added points in the centre of the domain selected the relevant variables, which were set using an orthogonal central composite design. A desirability function was defined. 

HPLC-HG-AFS To establish which factors are statistically significant, a Plackett-Burman experimental design, with type III resolution and four degrees of freedom, was carried out. The optimisation of the two relevant variables was accomplished by an orthogonal central composite design ( +star). 

For the experiment array, I prefer an orthogonal central-composite design (2), (3), which consists of three main parts, as shown in Table I. The first is a conventional 16-experiment fractional factorial design for five variables at two levels. The second comprises three identical experiments at the average, or center-point, conditions for the first 16 experiments. The final part comprises two out-lier experiments for each variable. These augment the basic two level design to provide an estimate of curvature for the response to each variable. The overall effect of the design is to saturate effectively the multi-dimensional variable space. It is more effective than the conventional "one-variable-at-a-time" approa.ch. 

Central composite orthogonal designs Quantitative Regression models of second order... 

These principles are illustrated by a central composite rotatable design in two variables. This design will be orthogonal with eight experiments at the center point... 

A central composite orthogonal design for three independent variables... 

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