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DOE is the process of planning of an experiment so that appropriate data are collected, which are suitable for further statistical analyses resulting in valid and objective conclusions.
The development of such an inverse model and the design of a sensor system to collect appropriate data are discussed in this study.
The model is amenable to rigorous experimental design, and when appropriate data are available, model parameters can be estimated using statistically robust multi-state capture-mark-recapture models.
In familiar modes of risk assessment, such as that performed in the insurance industry, vast amounts of appropriate data are available to precisely bound risks.
If appropriate, data are presented as mean ± standard deviation (SD) or as relative risk (RR).
This will be approached by further literature work and experimental measurements if appropriate data are not available.
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The algorithm how to choose appropriate data is proposed too.
In addition, where appropriate, data was analyzed by the 2-tailed student's t-test or 1-way and 2-way ANOVAs.
One way to generate sufficient amounts of appropriate data is automatic single-cell tracking using time-lapse video microscopy.
One of these challenges is to ensure that appropriate data is efficiently made available when it is required and where it is consumed.
Appropriate data is available.
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