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The first principal component explains 21% of the variation and separates European populations at one extreme from those of South Asia at the other.
The first principal component explains, respectively, 39% (EU), 40% (JP), and 44% (UK) of the proxies' variance.
From Table 3, it can be seen that the first three principal components together account for 78.38 % of the total variance in the dataset, in which the first principal component explains 44.09 % of the total variance, the second principal component exhibits 23.34 % of the total variance, and the third principal component explains 10.95 % of the total variance.
The first principal component explains 30 40% of the variance; the first and second principal components explain 55 60%.
The first principal component (along the x-axis) explains most of the total chi square, the second principal component explains second most of the total chi square.
The final principal component explains 7% of the variation, and identifies laying queens as the outgroup.
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The first principal component explained 74% of the variation that was mainly loaded by different temperature variables and altitude while the second principal component explained additional 23% mainly loaded by precipitation variables.
This spectrum is captured in a single principal component explaining 74% of variation in six key foliar traits within a global data set [GLOPNET; 13].
Plotting the variance explained versus principal components shows (Figure 4), that across the five Acheulean and three Oldowan sequences, the first principal component explained 25 40% of the variance in the data.
The first principal component explained 28.37%, the second principal component explained 21.41%, and the third principal component explained 15.58% of the molecular marker variation among 250 accessions.
The first principal component explained 31.3% the variance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com