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Reactions for most variables include production and decay.
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Our novel combination of temporal and spatial scales was the best overall model fit for most variables, including SRP where this model explained 54% of the variation.
Results indicated the effects of seasons and turf grass types and their interaction had significant effects on most variables including quality, season color, leaf texture, density, quality after clipping (p < 0.01).
Most variables included in the regression models had 1% or less missing.
Cases and test-negative controls were similar for most variables included in the study.
It can be highlighted that most variables included in the count model structure in fine govern the pmf.
As most variables included small sample numbers and thus not expected to be normally distributed, non-parametric tests were used.
All variables included in the analysis contained fewer than 5% missing data, and most variables included much less than 5% missing data.
When subjected to the more rigorous multivariate analysis, most variables – including age – fell out of statistical significance (Table 2), a phenomenon seen in many other geriatric studies.
In many administrative data-based studies, most variables, including primary outcomes, are based on the International Classification of Diseases (ICD), clinical modification codes (ICD-9 or ICD-10).
Another limitation is the use of self-reporting for most variables included in this study, which may have resulted in less reliable outcomes.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com