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Design of Experiments

Experimental design involves conducting a systematic series of tests to discover the relationship between the factors that affect a process and the response. The factors are varied in a systematic fashion and the resulting response observed. The results of the experiment are analyzed to find the regression equation that relates the factors to the response.

The Design of Experiments (Experimental Design) is used when a process is affected by several different factors. In an experimental design several factors are varied at the same time. This gives more information for less testing than the 'One Factor at a Time' (OFAT) method in which each factor is varied in turn whilst the others remain constant. It also allows 'interactions' between factors to be evaluated, and the effects of interactions are usually important.

The two main approaches to experimental design are the 'classical' and 'Taguchi' methods'. Experts are divided on the merits of the Taguchi method, but the emphasis on variation and the methods it uses to address variation are important. The types of design commonly used included Factorial Designs and Fractional Factorial Designs and Plackett-Burman Designs.

A more powerful approach is to use Response Surface Methods; this group includes the Box Behnken and Central Composite Designs (CCD).

Designs that involve mixtures require a different method of analysis, see the topic on Mixture Designs.

Terms used in the Design of Experiments include:

 Alias Fixed Effects Model Balanced Design Interaction Blocking Main Effects Coded factors Orthogonal Design Completely randomized design Random Effects Model Confounding Randomization Defining Relationship Randomized Block Dependent Variables Replication Design Generator Response Variable Design Resolution Rotatable Dispersion Screening Design EVOP Transformations Factor Treatment

 The Design of Experiments is covered in the MiC Quality basic Design of Experiments course and Advanced Design of Experiments course. Try out our courses by taking the first module of the Primer in Statistics free of charge.
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