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A series of models excluding each finding sequentially did not identify highly influential single findings.
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The statistical significance of each specific selection gradient, β i or γ i, was estimated using likelihood-ratio tests, by subtracting the log-likelihood for the model excluding each parameter, one at a time, from the log-likelihood of the full model, which is asymptotically chi-square distributed with one degree of freedom.
This method tests a series of regression models, excluding in each new model the worst predictor of the previously tested model according to a statistical criterion (p ≥.10).
Two separate models excluding respondents who had failed each test were run to investigate the effects on the model.
Tables 2, 3 and 4 provide regression coefficients, standard errors, and probability values for both models (excluding either grade or test) in each of the three content areas.
Based on the full model, we computed models excluding Blocking, Rhyme, Melody, Rhyme&Melody or the interactions of Blocking with each of the presentation modes, carrying forth the random slopes and intercepts of the full model.
A considerable number of protest responses may cause selectivity bias; consequently, we used models excluding protest zeros.
We also tested the models excluding insignificant independent variables.
A small number of models considering logistics aspects face a huge number of models excluding logistics.
Next, we fitted the same models excluding these individuals.
A reanalysis of residuals from regression models excluding humans yielded similar results.
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