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In addition, the assumption that data are missing at random may be implausible in such datasets, biasing results [ 4].
Nonetheless, a verb generated at random may be semantically distant from a given noun and, as such, is not necessarily creative.
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Note that although this arrangement is not strictly random, a random process may be used to select which components are used together at the starting point.
If the exposure variable X is not normally distributed, the joint normality assumption for the random intercept and random slope may be inappropriate.
In randomized trials, random error may be one reason for the early stopping of trials at interim analyses when benefit or harm appear to be significant [ 32, 40].
However, such random allocation may be subverted: either when trial subjects are selected before generation of the random allocation sequence; the random allocation sequence is directly observed, or when the random allocation sequence is correctly predicted before subject allocation.
In this situation, a log-normal random variable may be modeled as e X where X is a zero-mean, Gaussian random variable with variance σ.
If this variation is indeed random, it may be an evolutionary strategy to promote diversity.
For instance, a mixed regression model that incorporates random effects may be appropriate.
Hence, the subsequent price changes are not random and may be used to estimate future price changes.
To simulate pattern roughness or in other words texture, the random subreflectors may be spatially correlated [5, 25].
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CEO of Professional Science Editing for Scientists @ prosciediting.com