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The first discriminant root (LD1) explained 72.58 % of the observed variance, while the second (LD2) explained additional 26.02 % (Table 4).
Levene's test was used to determine the homogeneity of variance, while the normality examined using the Kolmogorov Smirnov test.
The first discriminant root (LD1) explained 51.93 % of the observed variance, while the second (LD2) contributed with 28.97 % (Table 9).
The first discriminant root (LD1) explained 40.15 % of the observed variance, while the second (LD2) explained 32.18 % (Table 13).
The performance test shows that CBMCC is effective and discriminative as it has the maximum intra-bit variance while the minimum inter-bit correlation.
The scope of this study is set for the mixture model of normal distributions with component specific variance, while the number of components is fixed.
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where is an -dimensional vector of AWGN samples with zero-mean and variance, while is the useful signal component, which is modeled as (2).
We hypothesize that this approach will capture a large portion of the genetic variance, while also providing further biological understanding of the traits in question.
They then seek to determine the optimal subgroup structure that explains the most variance while requiring the simplest specification of the model (the optimal balance between the most explanatory models and parsimonious models).
This study examined the best fit models using Akaike's Information Criterion; briefly, the selected models explained the greatest proportion of the variance while using the least number of explanatory variables to ensure the greatest precision, accuracy, and repeatability.
LCA methods typically include rules designed to find the subgroup solution that explains the most variance while requiring the simplest specification of the model.
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CEO of Professional Science Editing for Scientists @ prosciediting.com