Here are my thoughts regarding loss of experimental units. You don't say how many experimental units failed, so realize your options depend on this:
1. Is there another response variable(s) that quantifies the effect of the factors creating the lost extrusion(s)? I remember when working with Dr. Taguchi, he would say those treatments may be the most informative in the experiment.
2. As Pete says, you can't estimate quadratic effects with a 2-level factorial. You would need at least center points to estimate the departure from the linear assumption.
3. If you lost only one, try these options:
- Use the mean of the remaining treatments as a substitute for the missing run
- Use regression o estimate the missing run. Run analyze fit model, enter a model with 1 less DF (usually the highest order effect). Save the model (Red triangle>Save Columns>Prediction Formula). This will give you a prediction for the missing treatment.
- If you had predicted the results before running the experiment, try using your predicted result.
Do all 3 and see how well the results agree. If they are in relative agreement, you can be confident in the analysis. If not, then you have to think about running more runs. Of course the additional runs may be in a different inference space so be aware of the "block" effect.
"All models are wrong, some are useful" G.E.P. Box