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We found that quadratic models better explain the data than linear models for both epochs as well as for the early Eocene.
In our context, we explore PCA to understand which constructs better explain the data variance on bug localization results.
Throughout our data extraction and synthesis phases, we continually sought out further candidate theories that might better explain the data in the included studies.
Then we compared model M2a (selection) and M1a (nearly neutral) to test if invoking of positive selection in model M2a would better explain the data [ 31, 32].
Because fitting to the Hill model yielded somewhat atypical parameters, we explored whether an alternative model, which describes how large ligands bind to a linear array of sites (McGhee and von Hippel, 1974), might better explain the data.
A recent technical investigation of alternatives to the SCRalyze GLM has revealed that most models which explain more variance in the observed SCR data have worse sensitivity (Bach et al., 2013), despite their ability to better explain the data.
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Instead, an overwater colonization to the Caribbean in mid-Miocene better explains the data.
Coltheart and colleagues use the Bayesian model of abductive inference which invites us to ask two questions: Which hypothesis better explains the data?
Although a combination of three ETAS models for pre-swarm, swarm, and post-swarm periods better explains the data, a simpler model with an ETAS model and a boxcar function is even better.
We use both frequentist and Bayesian methods to determine the degree to which the non-null model better explains the data.
As in the undirected joint case, we can use both frequentist (likelihood ratio test) and Bayesian (BIC) methods to determine the degree to which each of these models better explains the data for Y than does the single-variable model for Y.
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