SQT Blog

Continuing his series of tips for people implementing Continuous Process Improvement, our tutor Albert Plant writes:

There is a natural tendency for experimenters to hurry to look at the effects graphs, ANOVA’s and other analysis, when they have completed their hard work running experiments, and they may forget to check the residuals. Residuals are unexciting, but contain crucial evidence on the validity of the experimental outcome. I have seen experimenters writing up lengthy reports on the outcome of their experiments, only to discover later that the condition of the residuals indicates that they have drawn incorrect conclusions. For example, an analysis of the residuals may indicate that it is necessary to transform the data to obtain the most appropriate model.

All DOE computer software, such as Minitab, provides extensive analysis of DOE residuals. Design Expert DOE computer software has no less than eleven separate graphs plus additional text output analysing the residuals. Clearly, the people who develop the mathematical tools for us to design and analyse our experiments are in no doubt as to the importance of the residuals in DOE.

Learn more about how design and analyse experiments and how to get the most information from the residuals, by attending our Design of Experiments training courses.

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I think that some experimenters believe that you should always be a case for using blocks when designing experiments. This is not so. There will be circumstances when it will be necessary to consider using blocks, but blocking doesn’t come cheaply. For example, four blocks will require three of your precious degrees of freedom, using up three experimental runs. Furthermore, blocking greatly reduces the range of randomization that can be used. In a blocked experiment randomization of runs can only be carried out within the blocks, instead of across the full set of experimental runs, as is the case in an un-blocked experiment.

Blocking can be useful when you are concerned that factors not included in the design may add variability to the design outcome. Such concern may arise, for example, where you need to divide the experimental runs among several operators, or you need to use two or more different pieces of equipment.

Try to avoid the need for blocking by ensuring that the running of the experiments is carried out with minimum changes among the factors not included in the design. For example, try to have the experiments carried out by just one operator; use the same piece of equipment for all runs; use the same piece of measuring equipment for all measurements from all runs, etc.

Learn more about the correct use of blocking designs by attending our Design of Experiments training courses.