Sentence examples for models corrected for from inspiring English sources
The dataset includes four sets of environmental variables related to biophysical conditions (net primary productivity models corrected for shallow-water reflectance, wave energy including sheltered-coastline corrections) and landscape context (coral reef and land cover within varying radii).
Both the logistic regression and linear regression models corrected for age, and Acute Physiology and Chronic Health Evaluation (APACHE) II score.
RESULTS Linear and logistic regression models, corrected for multiple testing, indicated that carriers of one or more of the S-alleles were more sensitive to stress than those with two L-alleles and at a higher risk for depression.
To (a) describe the Healthy Directions 2 study design and (b) identify baseline factors associated with whether participants opted for print or web-based materials.Hierarchical regression models corrected for clustering by physician were built to examine factors associated with choice of intervention modality.At baseline, just 4.0% of participants met all behavioral recommendations.
We then applied linear and logistic regression models corrected for matching in order to compare days from event to extubation, intensive care discharge, and hospital discharge in cases versus controls.[24] The day of event was defined as the day of VAC or VAP in cases, and the ventilator day on which the matched case patient developed VAC or VAP in controls.
All models corrected for clustering by clinic.
Unlike Poisson models, NBREG models correct for this over-dispersion by including a parameter that accounts for unobserved heterogeneity (Long and Freese 2001).
They estimate both cross-sectional and fixed effects models correcting for unobserved ability effects, but they do not perform separate analyses for natives and immigrants.
Such models correct for the correlation between repeated observations with subjects.
Multivariate models correcting for nesting of students within schools tested associations between demographics, TB symptoms, transmission, prevention knowledge, and intention to seek and adhere to treatment.
These fusion models correct for the biases inherent in atmospheric dispersion models, and also provide associated measures of uncertainty for the predictions, which are typically not available from dispersion models.
