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In Table 2, the odds ratios for the two conventional models, i.e., columns (1) and (2) show results from conventional models without controlling for spatial dependence.
We fit models without controlling for survey year, with similar results (data not shown).
The maximum variance explained was 69% (Table 4) and the predictive models (without controlling for phylogeny) explained 30% of the variance and prediction accuracy was 77% (Table 3).
Because season and blood pressure may be either collinear with the exposure or on the causal pathway, we examined the models without controlling for these covariates and found similar results.
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The first one was unadjusted model or model without controlling by other covariates.
We first selected the best-fitting model without controlling for potential confounders.
Results from the statistical model (without controlling for air pollutants) are reported.
Table 4 shows the unadjusted model without controlling for key area-level socioeconomic measures.
However, most associations between income inequality and mortality indicators were insignificant in the fixed effects models even without controlling for education and other country time-variant characteristics.
We fitted survival analysis models with and without controlling for the unobserved heterogeneity and we found that when this component was included, the models gave a significantly better fit.
We therefore present two fully-adjusted models with and without controlling for education.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com