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The above models provide statistics descriptive of the data.
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Some studies did not provide statistics on the percentage of variance the model explained[ 59, 66].
Based on the χ 2 test statistics, our models provide a better fit to the data than those in the Berry et al. paper.
These different models provide the user with varied information regarding association statistics and tests of stratification.
The deterministic trend model is adopted, and MCMC iterations 25,000-26,000 are cumulated over the nine models, providing posterior summary statistics (mean, variance, 2.5% percentile, 97.5% percentile) for cross-validatory predicted extents in 2010-11 across the entire Arctic region.
The goal of this test is to determine if the baseline model structure is the same for all three cohorts; further, this model provides baseline statistics for subsequent model comparisons with more strict constraints (see Table 2).
To any doubters, Ciaccia provides statistics.
Table 2 provides statistics per zone.
Summarizing the obtained results, probabilistic imputation models better preserve the multivariate distribution of the data and marginal statistics, whereas regression models provides greater accuracy of imputed values; and hot-deck methodologies give real values with marginal distributions that are closer to the original ones.
The goodness-of-fit statistics calculated after fitting the survey design adjusted logistic regression models provided no evidence of lack of fit of any of the models (p>0.05).
Standard curves and unknowns were plotted and determined respectively using a non-linear regression, with variable slope sigmoidal curve fitting model provided by the GraphPad Prism statistics software.
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