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Count data may be described by a Poisson regression model.
The translation algorithm for converting the 1986 criteria illness rate to an equivalent illness rate from the NEEAR data may be described as a two step process.
Second, the absence of dietary data may be described as a study limitation given the associations between diet, sleep and hyperglycaemia, which is mediated through appetite-associated hormones.
The observed data may be described by a latent class model which assumes that the standard test (T1) and new test (T2) are imperfect measures of an underlying latent variable D, or true disease status.
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If not, then these data may be describing some form "fractional kill" or stochastic resistance, akin to that shown in Spencer et al., Nature, 2009.
Such data has been referred to as "zero-inflated data" and may be described using two-part models.
Thus, a data set may be described without loss of information or by means of any desired granularity.
First, our method of data collection may be described as over-simplistic.
Every entry in the database is accompanied by at least one 'Reference ID.' The data at a specific depth or time may be described in multiple publications or different data types may have been published in different articles, e.g., paleomagnetic, rock magnetic, and chronological data, such as for Birkat Ram, Israel (Frank et al. 2002 20033; Schwab et al. 2004).
The obtained data on external mass transfer coefficient may be described by the common criterial equation Sh = 0.0695Re0.93Sc1/3.
It is thus possible that mutations at the sites considered by Rouzine and Coffin may correspond to s = 0.1, whereas those in the patient data of Shankarappa et al. may be described by s = 0.001.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com