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Finally, in a random model we must take into account the heterogeneity of studies.
According to this logic, in a random model, it is not possible to give more weight to a study rather than another.
In a random model, the expected number of friends that have collected an item is directly proportional to the degree (k_{alpha}) of the item.
The fCAI represents a log-odds-ratio, which compares the odds to find the number of linking interactions in the experimental set to the odds in a random model (see discussion S1).
This discrepancy is somewhat smaller when we make such a comparison in a random model, 97 (96, 100) versus 50.
We assessed the estimated effect size in a random model meta-analysis because it incorporates heterogeneity among studies.
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In Type2, a random model with a threshold is implemented with the function 1 δ.
Dependence of nearest neighbor nucleotides in a random walk model can be modeled using a first or second order Markov chain.
The proportion of each layer to total thickness was modeled statistically with scan session as a variable and the change in absolute thickness of each layer per unit change in body weight was modeled in a random regression model.
Setting the differencing degree d=0 in ARIMA model will result in ARMA model, while setting p=q=0, d=1 results in a random walk model.
A value of results in a random walk model of and results in a white noise model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com