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5. Process Improvement
5.5. Advanced topics
5.5.9. An EDA approach to experimental design Cumulative residual standard deviation plot

Motivation: How do we Construct a Good Model?

Models for 2k and 2k-p designs Given that we have a statistic to measure the quality of a model, any model, we move to the question of how to construct reasonable models for fitting data from 2k and 2k-p designs.
Initial simple model The simplest such proposed model is
    \( Y = c + \epsilon \)
that is, the response Y = a constant + random error. This trivial model says that all of the factors (and interactions) are in fact worthless for prediction and so the best-fit model is one that consists of a simple horizontal straight line through the body of the data. The least squares estimate for this constant c in the above model is the sample mean \( \bar{Y} \). The prediction equation for this model is thus
    \( \hat{Y} = \bar{Y} \)
The predicted values \( \small \hat{Y} \) for this fitted trivial model are thus given by a vector consisting of the same value (namely \( \bar{Y} \)) throughout. The residual vector for this model will thus simplify to simple deviations from the mean:
    \( Y - \bar{Y} \)
Since the number of fitted coefficients in this model is 1 (namely the constant c), the residual standard deviation is the following:
    \( s_{res} = \sqrt{\frac{\sum_{i=1}^{n}{(Y_{i} - \bar{Y})^2}}{n - 1}} \)
which is of course the familiar, commonly employed sample standard deviation. If the residual standard deviation for this trivial model were "small enough", then we could terminate the model-building process right there with no further inclusion of terms. In practice, however, this trivial model does not yield a residual standard deviation that is small enough (because the common value \( \bar{Y} \) will not be close enough to some of the raw responses Y) and so the model must be augmented--but how?
Next-step model The logical next-step proposed model will consist of the above additive constant plus some term that will improve the predicted values the most. This will equivalently reduce the residuals the most and thus reduce the residual standard deviation the most.
Using the most important effects As it turns out, it is a mathematical fact that the factor or interaction that has the largest estimated effect
    \( \hat{E} = \bar{Y}(+) - \bar{Y}(-) \)
will necessarily, after being included in the model, yield the "biggest bang for the buck" in terms of improving the predicted values toward the response values Y. Hence at this point the model-building process and the effect estimation process merge.

In the previous steps in our analysis, we developed a ranked list of factors and interactions. We thus have a ready-made ordering of the terms that could be added, one at a time, to the model. This ranked list of effects is precisely what we need to cumulatively build more complicated, but better fitting, models.

Step through the ranked list of factors Our procedure will thus be to step through, one by one, the ranked list of effects, cumulatively augmenting our current model by the next term in the list, and then compute (for all n design points) the predicted values, residuals, and residual standard deviation. We continue this one-term-at-a-time augmentation until the predicted values are acceptably close to the observed responses Y (and hence the residuals and residual standard deviation become acceptably close to zero).

Starting with the simple average, each cumulative model in this iteration process will have its own associated residual standard deviation. In practice, the iteration continues until the residual standard deviations become sufficiently small.

Cumulative residual standard deviation plot The cumulative residual standard deviation plot is a graphical summary of the above model-building process. On the horizontal axis is a series of terms (starting with the average, and continuing on with various main effects and interactions). After the average, the ordering of terms on the horizontal axis is identical to the ordering of terms based on the half-normal probability plot ranking based on effect magnitude.

On the vertical axis is the corresponding residual standard deviation that results when the cumulative model has its coefficients fitted via least squares, and then has its predicted values, residuals, and residual standard deviations computed. The first residual standard deviation (on the far left of the cumulative residual standard deviation plot) is that which results from the model consisting of

  1. the average.
The second residual standard deviation plotted is from the model consisting of
  1. the average, plus
  2. the term with the largest |effect|.
The third residual standard deviation plotted is from the model consisting of
  1. the average, plus
  2. the term with the largest |effect|, plus
  3. the term with the second largest |effect|.
and so forth.
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