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We originally included tobacco use as a predictor because of its status as a known CYP450 inducer (Appendix 1).
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Vegetation is generally preferred as a source of potential predictors because of having a more direct link with reproductive necessities of species than topography and climate.
We were unable to evaluate interactions between predictors because of insufficient degrees of freedom (see Table 2 for the models considered, AICC values, and Akaike weights).
We excluded age at diagnosis of diabetes and duration of diabetes from the list of potential predictors because of their high degree of collinearity with age.
Linear regression analysis was not a suitable statistical test for independent predictors, because of the non-normal distributions of UKCAT and A level scores.
Not surprisingly, both BCL6 and JAW1 were selected to be the significant prognostic predictors because of their importance involved in the underlying pathogenic mechanisms for lymphoma.
Since T levels before and after the experiment were highly correlated, r = .92, p < .00001, regressions did not include both predictors because of multicollinearity.
Variables were not included as candidate predictors because of colinearity (n=61), a low cell count (<5) in the χ test (n=11), lack of a consistent relation with pre-eclampsia in literature (n=4), or not readily applicable to a future obstetric population (n=20).
This may seem small for the most important predictor, but because of associations between predictors, there is substantial forecasting power that cannot be cleanly attributed to single predictors.
Thus, creatinine level may be a good predictor, also because of its relatively greater stability around the time of CDI diagnosis in comparison to leukocytosis.
Insulin is reported as the primary predictor because it explained more of the variance in PWV measures than HOMA-IR in the SAVE trial of individual with fasting glucose levels within the normal range.
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