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We examine in depth the quality of data from a random selection of studies, from an epidemiological and statistical point of view.
The approach is demonstrated for three phenomenological drag models from the literature using data from a random Bialecki ring packing under counterflow conditions.
Normally, it would be all but impossible to get coherent data from a random sediment sample — the strands of DNA are too jumbled and degraded to be isolated for sequencing.
Using data from a random sample of 12,935 members from the core Danish work force the hypotheses were tested in a cross-sectional design utilising ordered logistic regression models.
In this study, data from a random sample of Danish consumers are used to test the hypothesis that the emergence of a sustainable consumption pattern is influenced by individual value priorities.
Data from a random sample of the study subjects at baseline show that approximately one-half of the males and one-third of the females were overweight (BMI ≥ 25 kg/m2), and approximately one-quarter of all participants reported that they walked at least 4 h/week.
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We present the eye data from a randomized side (using a random number generator) for each patient.
Our data collection method is based on manual data extraction from a random sample of 60 80 PMRs per hospital.
Data were entered into the study database (MM), with data entry from a random 10%% of participants duplicated by a researcher not associated with the project in order to check for accuracy.
A control data set, from a random design, was applied for validation of the calibration models.
Data are from a random phone survey conducted in the Portland, Oregon, region.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com