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The distribution of basal area data was skewed and hence log converted to improve assumptions of normality and focus on relative quantities of species (McCune et al. 2002).
Thus, various sources of independent data can be used to augment evidence for the involvement of a particular gene and improve assumptions regarding causal associations.
To improve assumptions of normality for statistical analysis, we examined the distribution of population means using untransformed, ln (or ln + 1), square-root, arcsin square-root, or e x transformations and chose the best fit to normality according to the Shapiro Wilk statistic (Shapiro and Wilk 1965).
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Moreover, we improve several assumptions on the involved mappings.
In order to improve the assumption of normality, we used a quadratic transformed MDWG and an inverse transformed FCR in the analyses.
Behavioral economists fiddle with it, improving the assumptions and making them psychologically sensible.
That is why new and better observations are needed to improve the fundamental assumptions on which the models are based.
Several growth traits were log-transformed to improve fit to assumptions of ANOVA (indicated in Table 1).
The distribution of radon concentrations was skewed, so we used natural log-transformed radon concentration (ln-radon) as our outcome variable to improve compliance with assumptions of linear regression (i.e., homoscedasticity and normality of residuals).
As statistical tests and corrections for violations of basic phylogenetic assumptions improve [ 28, 41- 44], and as hypotheses about the 'transferability' of different types of gene are refined [ 2, 7, 9, 45], the underlying organismal history initially suggested by 16S rRNA analysis and refined using other data sets should solidify.
These solutions are used to improve the initial assumption of the interface temperature until convergence.
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