Open AccessA sequential experimentation method to separate interaction effects from block effects
Author(s) -
Ríos Armando J.,
Zarco Murillo Hazael,
Pantoja Yaquelin V.,
Tapia Esquivias Moisés
Publication year - 2021
Publication title -
engineering reports
Language(s) - English
Resource type - Journals
ISSN - 2577-8196
DOI - 10.1002/eng2.12289
Subject(s) - blocking (statistics) , noise (video) , block (permutation group theory) , computer science , table (database) , main effect , blocking effect , factor (programming language) , statistics , econometrics , mathematics , psychology , artificial intelligence , data mining , developmental psychology , geometry , image (mathematics) , programming language
Abstract Blocking is a basic experimentation principle that separates the variability caused by noise factors. Unless the experiment is replicated, blocking produces loss of information, particularly two‐factor interactions ( 2FIs ) are commonly lost to blocks. Additionally, the ANOVA table does not show to what extent each blocked noise factor affects the response variable. Individual contributing percentages for noise factors can be useful to make process improvements and to understand which noise factors are most influential. This research proposes a sequential experimentation method to separate 2FIs from blocks and assign contributing percentages to each blocked noise factor. The method is evaluated and compared to foldover, semifold, and D ‐optimal augmentation.