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This construction almost meets Assumption 1, since only H ― k is required to generate y k.
It turned out that the distance between the points was not meeting assumptions of the method.
The primary dependent variables met assumptions for analysis of variance (ANOVA), and were approximately normally distributed (|skewness| <2.0, |kurtosis| <2.0).
For ANOVA on the time to second-degree burn the data was log10 transformed to meet assumptions of normality.
The primary dependent variables met assumptions for ANOVA, and were approximately normally distributed (|skewness| < 2.0, |kurtosis| < 2.0).
All other data met assumptions of parametric statistics.
Data were arcsine-transformed to meet assumptions where necessary.
LCC scores were log transformed to meet assumptions of normality and heteroscedasicity.
To meet assumptions of normality for imputation models, MVL was log-transformed.
When appropriate, variables were transformed with power functions to meet assumptions of normality.
Larval masses were log-transformed to meet assumptions of normality and homogeneity of variance.
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CEO of Professional Science Editing for Scientists @ prosciediting.com