Selection of Factors, Levels and Basic Level

Having selected the system response, we start choosing factors, levels of the factors and center point of the design (basic level or the null point). By factor we understand the controllable independent variable that corresponds to one possibility of influence on the object of research. A factor is considered defined if its name and domain of factors are determined. A factor may take several values in this field. The chosen factor values, both qualitative and quantitative, are called factor variation levels. Factor variation levels in the design of experiments are coded values. Under factor interval of variation we understand the difference between two factor levels, which in their coded form have value one. When selecting the factors one should pay attention to the conditions they must meet. 

The controllable requirement of factors is linked to the possibility of setting them on several levels and maintaining those levels precise enough. Or, by changing factor values, one changes the research subject status or controls the subject. 

Factor singularity means its property to directly change the status of a research subject, i.e. it is not a function of other factors and it may be fixed to any value in the domain of factors. 

Factor concordance is a property that makes it possible for all factor combinations to be realized in an experiment. This property is very important when an experiment with several simultaneous factor variations is designed. It is not a rare case where the lack of this property brings about a change in defining a research problem, excluding some factors from the experiment, or it changes the domain of factors. 

In practice we are often faced with a research subject that has several technological phases and where the response is measured in its last phase. In that case, the subject is studied cybernetically as a black box , like a unique technological phase with all the factors that corresponded to individual technological phases. We had no responses by individual technological phases in this case, but this may occur. Moreover, response optima by individual phases contradict the general optimum system. This indicates that optimization by individual phases of a research subject is justified and possible. In this way it is possible to incorporate into the design of an experiment, factors from various phases of a research subject, but this is not always necessary. 

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