Lecture

Fractional factorial design: Basics and Applications

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Description

This lecture covers the fundamentals of fractional factorial design, including the group structure of factorial columns, blocking alias, interaction effects, learning outcomes, and the comparison between full factorial and fractional designs. It also delves into the geometrical interpretation of the 2(4-1) design and the aliases of the 24-1 design, showcasing the importance of selecting, using, and analyzing fractional factorial designs to optimize experimental outcomes.

Instructor

Jan Van Herle

Born in Antwerp, Belgium. In Switzerland since 1983. Became Swiss citizen in 2004 out of conviction of principles of democracy and bottom-up participation. No double nationality. Village Council Member for 2 five-year mandates in 2006-2016. 1987 : Chemist from Basel University (CH). 1988 : Post-graduate IT diploma from Basel Engineering School. 1989 : Industry internship ABB Baden (CH). 1990-1993 : PhD Thesis EPFL, on Solid Oxide Fuel Cell cathode reaction mechanisms. 1994-1995 : Japanese Postdoctoral Fellowship in Tsukuba, Japan, on ceramic powders. 1995-2000 : Researcher at EPFL, Dpt. Chemistry : project responsible in PPM2 (materials), FP4-BriteEuram, NEDO (Japan), Swiss Gas Union (CH, oxygen membranes). 1998-2000 : Masters in Energy Technology, EPFL. 2000 : Cofounder of HTceramix SA (EPFL spin-off), now based in Yverdon (14 employees). Taken over by SOLIDpower in 2007, now 250 employees with 70 MCHF raised. 2000 : 1st Assistant and lecturer at LENI (STI-IGM) : fuel cell group responsible, projects on biogas (Federal Energy Office), woodgas (CCEM), fuel cell stacking (CTI, FP6, FNS), ceramic separation membranes (COST, FNS), microtubes (STI Seed), stability/lifetime/reliability in fuel cells (Electricité de France, swisselectric research). Currently 4 Ph D theses ongoing, 14 theses concluded, of which 5 colateral with SB and IMX. M.E.R. since Nov 2008. Total funding raised so far >18 MCHF (50% as main applicant; 30% outside CH; 20% industry). Scientific output : >135 peer-reviewed publications, >120 conference papers, 40 invited presentations (8 keynotes), >70 granted proposals. Fluent in 5 languages (Dutch, French, German ( Swiss-german), English, Spanish).

Official source

Related concepts (14)

Factorial experiment

In statistics, a full factorial experiment is an experiment whose design consists of two or more factors, each with discrete possible values or "levels", and whose experimental units take on all possible combinations of these levels across all such factors. A full factorial design may also be called a fully crossed design. Such an experiment allows the investigator to study the effect of each factor on the response variable, as well as the effects of interactions between factors on the response variable.

Fractional factorial design

In statistics, fractional factorial designs are experimental designs consisting of a carefully chosen subset (fraction) of the experimental runs of a full factorial design. The subset is chosen so as to exploit the sparsity-of-effects principle to expose information about the most important features of the problem studied, while using a fraction of the effort of a full factorial design in terms of experimental runs and resources.

Central composite design

In statistics, a central composite design is an experimental design, useful in response surface methodology, for building a second order (quadratic) model for the response variable without needing to use a complete three-level factorial experiment. After the designed experiment is performed, linear regression is used, sometimes iteratively, to obtain results. Coded variables are often used when constructing this design.

Interaction (statistics)

In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the effect of one causal variable on an outcome depends on the state of a second causal variable (that is, when effects of the two causes are not additive). Although commonly thought of in terms of causal relationships, the concept of an interaction can also describe non-causal associations (then also called moderation or effect modification).

Design of experiments

The design of experiments (DOE or DOX), also known as experiment design or experimental design, is the design of any task that aims to describe and explain the variation of information under conditions that are hypothesized to reflect the variation. The term is generally associated with experiments in which the design introduces conditions that directly affect the variation, but may also refer to the design of quasi-experiments, in which natural conditions that influence the variation are selected for observation.

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