factorial design


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factorial design

[fak′tȯr·ē·əl di‚zīn]
(statistics)
A design for an experiment that allows the experimenter to find out the effect levels of each factor on levels of all the other factors.
References in periodicals archive ?
Based on the preliminary data, a [3.sup.2] factorial design was used to optimize the amount of Eudragit[R]-EPO (X1) and Pluronic[R] F-68 (X2) and identify the independent variable affecting the drug content and the percentage drug encapsulation efficiency (dependent variable).
All analyses considered 90% confidence level (p < 0.1) according to Box, Hunter and Hunter (1978) and Rodrigues and Iemma, (2014) for fractional factorial design [2.sup.5-1].
In a fractional factorial design, the factors are confounded, but this influence can be neglected.
Based on the variety of experimental procedures reported or producing MOF-199 and the absence of information in literature concerning the importance of understanding the significant experimental conditions for MOF-199 production, the aim of this work was to apply a [2.sup.3] factorial design to evaluate the influence of [Cu.sup.2+] salts counterions, acetate (OAc) and chloride ([Cl.sup.-]), and the synthesis parameters (time, temperature and metal concentration) on the reaction yield (response variable) of MOF-199.The obtained compounds were characterized by infrared vibrational spectroscopy (FT-IR spectroscopy), thermogravimetric analysis (TGA) and powder X-ray diffraction (XRD).
Factorial design is a tool that allows experimentation on many factors simultaneously.
According to the results obtained in the Design 1, a new factorial design 22 (Design 2) was carried out, varying the temperature (5 to 15[degrees]C) and using the same pressure (1 to 3 bar).
For the purpose, full factorial design (FFD) is considered an effective statistical approach to optimize experimental conditions with minimum experimental data [16].
The factorial design approach can accelerate learning about APMs because it provides for simultaneous testing of multiple versions of a model.
The center points allowed the evaluation of the full factorial design model linearity and the experimental errors.
The factorial designs determine which factors have the important effects on the response and how the effect of one factor varies with the level of the other factors.
This relationship was achieved by using a factorial design of experiments, which provides an approach to the study of a process with a large number of variables involved such as reinforcement corrosion in reinforced concrete mortar or concrete.
To test a large number of additives, we chose a factorial design approach over one-factor-at-a-time testing making use of a software package, Design-Expert[R] developed by Stat-Ease Inc.