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Now that all universities employ similar tactics, there is little variation in the data gathered.
For example, the three-variable probability model that I referred to above explains less than ten per cent of the variation in the data.
Many techniques destroy the natural variation in the data and treat outliers as anomalies to be edited or ignored.
On average, 24% of the total variation in the data was due to measurement error.
Individual variation in the data gave no significant difference in preference between the two automatic bowls.
The model explained 61.2% of the systematic variation in the data.
Together PC1 and 2 represent 36% of total variation in the data.
Figure 4 summarizes the availability of this variation in the data.
A greater F-value indicates that the factors adequately explain the variation in the data.
The first canonical axis accounted for 77%% of the variation in the data set.
The best diagram is the one that captures all meaningful variation in the data.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com