The Two-Level Factor Structures for Variables principle defines a hierarchical decomposition method within statistical experimental design where variables are partitioned into primary factors and their corresponding levels to isolate independent effects. This theoretical framework formally establishes the structural conditions under which interaction terms can be modeled by arranging input parameters across two distinct strata, ensuring orthogonality between main effects and interactions. It serves as a foundational mechanism in Design of Experiments (DoE) theory for managing multivariate complexity without requiring full enumeration of all possible combinations simultaneously.
Prerequisite chain context: requires Full Factorial Design in DoE using DataTab.