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Quantitative methods for optimizing product attribute levels using preference elicitation (e.g., conjoint) data are useful for many product types.
Include in your article reasons why such data are useful and important.
Also, the sensors' data are useful for predicting system operation and network failures.
These realistic data are useful for design engineers for outdoor assessment of PV system components.
These data are useful to get insights into participants' cognitive processes during reasoning tasks.
These data are useful for future trial design in this unique population.
The preclinical data are useful for the design of clinical trials of felotaxel.
However, the experimental data are useful in providing a baseline for the simulation results to be compared.
The data are useful for multiple purposes of assessment, including screening, eligibility determination, instructional development, and program evaluation.
Epidemiological data are useful to determine changes in forms of clinical expression and in the microbial agents causing infections.
The obtained data are useful for the design of cultivation schemes for pDNA production by E. coli.
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CEO of Professional Science Editing for Scientists @ prosciediting.com