5.
Process Improvement
5.3. Choosing an experimental design 5.3.3. How do you select an experimental design?


Response surface models may involve just main effects and interactions or they may also have quadratic and possibly cubic terms to account for curvature 
Earlier, we described the response surface method (RSM) objective. Under some
circumstances, a model involving only main effects and interactions
may be appropriate to describe a response surface when


Equations for quadratic and cubic models 
In other circumstances, a complete description of the process behavior
might require a quadratic or cubic model:
Quadratic
Cubic
These are the full models, with all possible terms, rarely would all of the terms be needed in an application. 

Quadratic models almost always sufficient for industrial applications  If the experimenter has defined factor limits appropriately and/or taken advantage of all the tools available in multiple regression analysis (transformations of responses and factors, for example), then finding an industrial process that requires a thirdorder model is highly unusual. Therefore, we will only focus on designs that are useful for fitting quadratic models. As we will see, these designs often provide lack of fit detection that will help determine when a higherorder model is needed.  
General quadratic surface types 
Figures 3.9 to 3.12 identify the general quadratic surface types that
an investigator might encounter


Factor Levels for HigherOrder Designs  
Possible behaviors of responses as functions of factor settings 
Figures 3.13 through 3.15 illustrate possible behaviors of responses
as functions of factor settings. In each case, assume the value of
the response increases from the bottom of the figure to the top and that
the factor settings increase from left to right.


A twolevel experiment with center points can detect, but not fit, quadratic effects  If a response behaves as in Figure 3.13, the design matrix to quantify that behavior need only contain factors with two levels  low and high. This model is a basic assumption of simple twolevel factorial and fractional factorial designs. If a response behaves as in Figure 3.14, the minimum number of levels required for a factor to quantify that behavior is three. One might logically assume that adding center points to a twolevel design would satisfy that requirement, but the arrangement of the treatments in such a matrix confounds all quadratic effects with each other. While a twolevel design with center points cannot estimate individual pure quadratic effects, it can detect them effectively.  
Threelevel factorial design  A solution to creating a design matrix that permits the estimation of simple curvature as shown in Figure 3.14 would be to use a threelevel factorial design. Table 3.21 explores that possibility.  
Fourlevel factorial design  Finally, in more complex cases such as illustrated in Figure 3.15, the design matrix must contain at least four levels of each factor to characterize the behavior of the response adequately.  
3level factorial designs can fit quadratic models but they require many runs when there are more than 4 factors 


Fractional factorial designs created to avoid such a large number of runs  Twolevel factorial designs quickly become too large for practical application as the number of factors investigated increases. This problem was the motivation for creating 'fractional factorial' designs. Table 3.21 shows that the number of runs required for a 3^{k} factorial becomes unacceptable even more quickly than for 2^{k} designs. The last column in Table 3.21 shows the number of terms present in a quadratic model for each case.  
Number of runs large even for modest number of factors 
With only a modest number of factors, the number of runs is very
large, even an order of magnitude greater than the number of parameters
to be estimated when k isn't small. For example, the absolute
minimum number of runs required to estimate all the terms present in a
fourfactor quadratic model is 15: the intercept term, 4 main effects,
6 twofactor interactions, and 4 quadratic terms.
The corresponding 3^{k} design for k = 4 requires 81 runs. 

Complex alias structure and lack of rotatability for 3level fractional factorial designs 
Considering a fractional factorial at three levels is a logical step,
given the success of fractional designs when applied to twolevel
designs. Unfortunately, the alias structure for the threelevel
fractional factorial designs is considerably more complex and harder
to define than in the twolevel case.
Additionally, the threelevel factorial designs suffer a major flaw in their lack of 'rotatability.' 

Rotatability of Designs  
"Rotatability" is a desirable property not present in 3level factorial designs  In a rotatable design, the variance of the predicted values of y is a function of the distance of a point from the center of the design and is not a function of the direction the point lies from the center. Before a study begins, little or no knowledge may exist about the region that contains the optimum response. Therefore, the experimental design matrix should not bias an investigation in any direction.  
Contours of variance of predicted values are concentric circles  In a rotatable design, the contours associated with the variance of the predicted values are concentric circles. Figures 3.16 and 3.17 (adapted from Box and Draper, `Empirical Model Building and Response Surfaces,' page 485) illustrate a threedimensional plot and contour plot, respectively, of the `information function' associated with a 3^{2} design.  
Information function 
The information function is:
with V denoting the variance (of the predicted value \( \hat{y} \). Each figure clearly shows that the information content of the design is not only a function of the distance from the center of the design space, but also a function of direction. 

Graphs of the information function for a rotatable quadratic design 
Figures 3.18 and 3.19 are the corresponding graphs of the information
function for a rotatable quadratic design. In each of these figures,
the value of the information function depends only on the distance
of a point from the center of the space.


Classical Quadratic Designs  
Central composite and BoxBehnken designs  Introduced during the 1950's, classical quadratic designs fall into two broad categories: BoxWilson central composite designs and BoxBehnken designs. The next sections describe these design classes and their properties. 