Results 291 to 300 of about 4,561,954 (333)
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Journal of the Franklin Institute, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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2020
Having an understanding of the factors that can bias or confound our experiments leads us to a consideration of the experimental design.
Michael Parkinson +1 more
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Having an understanding of the factors that can bias or confound our experiments leads us to a consideration of the experimental design.
Michael Parkinson +1 more
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1970
We shall discuss in this chapter some statistical methods for detecting changes in the response due to changes effected in the controllable variables assuming that the measurements are subject to random errors of measurement or changes in the unknown variables during the period of experimentation.
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We shall discuss in this chapter some statistical methods for detecting changes in the response due to changes effected in the controllable variables assuming that the measurements are subject to random errors of measurement or changes in the unknown variables during the period of experimentation.
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1980
In earlier chapters, I have discussed a very simple experiment on two treatments. In many practical situations, the subjects or materials fall naturally into groups, or can be organized as groups.
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In earlier chapters, I have discussed a very simple experiment on two treatments. In many practical situations, the subjects or materials fall naturally into groups, or can be organized as groups.
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2018
In the case of lack of theoretical knowledge of the relationships between input and outputs variables of a system, we have a second possibility: the experimental reconstruction of these relationships. In the case of theoretical knowledge, the system is represented by linear or nonlinear equations between input and output variables, while in the absence
Alessandro Freddi, Mario Salmon
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In the case of lack of theoretical knowledge of the relationships between input and outputs variables of a system, we have a second possibility: the experimental reconstruction of these relationships. In the case of theoretical knowledge, the system is represented by linear or nonlinear equations between input and output variables, while in the absence
Alessandro Freddi, Mario Salmon
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2009
Abstract After the failure analysis team hypothesizes failure causes, prepares a failure mode assessment and assignment, and evaluates all potential failure causes, it may find in some cases that several causes are still suspect but cannot be confirmed. In this situation, an experiment is necessary to confirm or rule out suspected causes.
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Abstract After the failure analysis team hypothesizes failure causes, prepares a failure mode assessment and assignment, and evaluates all potential failure causes, it may find in some cases that several causes are still suspect but cannot be confirmed. In this situation, an experiment is necessary to confirm or rule out suspected causes.
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Gut microbiota in human metabolic health and disease
Nature Reviews Microbiology, 2020Yong Fan, Oluf Borbye Pedersen
exaly
1990
Design of experiments consists of a series of techniques used to explore various operating conditions of a process with the goal of finding lower raw material, process, and product costs, improved or new product performance, less product variability, or higher production capacity to meet increasing demand.
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Design of experiments consists of a series of techniques used to explore various operating conditions of a process with the goal of finding lower raw material, process, and product costs, improved or new product performance, less product variability, or higher production capacity to meet increasing demand.
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