TY - GEN
T1 - Comparing different approaches for design of experiments (DoE)
AU - Tanco, Martín
AU - Viles, Elisabeth
AU - Pozueta, Lourdes
PY - 2009
Y1 - 2009
N2 - Design of Experiments (DoE) is a methodology for systematically applying statistics to experimentation. Since experimentation is a frequent activity at industries, most engineers (and scientists) end up using statistics to analyse their experiments, regardless of their background. OFAT (one-factor-at-a-time) is an old-fashioned strategy, usually taught at universities and still widely practiced by companies. The statistical approaches to DoE (Classical, Shainin and Taguchi) are far superior to OFAT. The aforementioned approaches have their proponents and opponents, and the debate between them is known to become heated at times. Therefore, the aim of this paper is to present each approach along with its limitations.
AB - Design of Experiments (DoE) is a methodology for systematically applying statistics to experimentation. Since experimentation is a frequent activity at industries, most engineers (and scientists) end up using statistics to analyse their experiments, regardless of their background. OFAT (one-factor-at-a-time) is an old-fashioned strategy, usually taught at universities and still widely practiced by companies. The statistical approaches to DoE (Classical, Shainin and Taguchi) are far superior to OFAT. The aforementioned approaches have their proponents and opponents, and the debate between them is known to become heated at times. Therefore, the aim of this paper is to present each approach along with its limitations.
KW - Classical
KW - Design of Experiments
KW - Shainin
KW - Statistical approach
KW - Taguchi
UR - http://www.scopus.com/inward/record.url?scp=78651542499&partnerID=8YFLogxK
U2 - 10.1007/978-90-481-2311-7_52
DO - 10.1007/978-90-481-2311-7_52
M3 - Conference contribution
AN - SCOPUS:78651542499
SN - 9789048123100
T3 - Lecture Notes in Electrical Engineering
SP - 611
EP - 621
BT - Advances in Electrical Engineering and Computational Science
ER -