In experimental design theory, plans with two or three levels of influence are the most often employed. The total factor experiment includes N = 2k experiments with two levels of change in the influencing variables, and N = 3k experiments with three levels of change in the influencing factors. The 2k plan, in particular, is often utilized throughout the early stages of research and even while conducting explorations with a high number of influencing variables. The number of influencing variables, for example, would be k = 15, and the number of experiments, N = 215 = 32768. Not just on actual devices, but also in computer simulations, this is virtually impossible. As a result, the number of experiments must be reduced to a manageable amount. The method of influencing factors space reduction can be implemented in the following ways. (i) Preliminary influencing factor analysis is used to screen for influencing factors with no or little value, i.e., variables that have little or no impact on the objective function. (ii) Using partial plans (or, as the theory of experimental planning calls them, partial responses) when the number of variables remaining after screening for null factors is still very large. This article will discuss one method for doing a preliminary screening of the initial influencing factors in order to identify those that have either no influence at all or a small effect on the objective function that is the subject of the investigation.