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All models weighted by bank-firm debt in 2008:Q4.
Each univariable effect size was pooled and computed in a separate meta-analysis using random-effects models weighted by inverse variance.
For this reason, we show regression models weighted by sample size.
All analyses were conducted using maximum likelihood, random-effects models weighted by the inverse of the variance.
Evidence of an age gradient was assessed by fitting linear regression models (weighted by the number of observations).
Bayesian model averaging averages posterior parameter estimates over models, weighted by the posterior model probabilities (Penny et al., 2010).
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The predictions were computed as weighted averages of the predictions obtained from each candidate model weighted by the weight or probability assigned to that model.
To derive predicted local extinctions and colonisations, we averaged the predictions from each sub model
Univariable effect size was pooled and computed in a meta-analysis using random-effects model weighted by inverse variance.
A linear regression model, weighted by inverse variance, was applied to assess the treatment effects for continuous outcomes.
The standard curve, which ranged from 5 to 2500 fmol per one million cells (fmol/106 cells), was fitted to a quadratic regression model weighted by 1/concentration.
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