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This means that 83% of the data are explained by the estimated regression equation.
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In Section 4, the data is explained and descriptive statistics are provided.
Overall, 59.24% of the total variance in the data is explained.
This value expresses what fraction of the variance of the data is explained by the fitted trend line.
The coefficient of determination (R2 = 0.68), suggests that 68% of the variability in the data was explained by Eq. 2.
This result proves that this multiple regression model fits the data and 35.4% of the variance in the data is explained by this model.
To determine how much variance in the data was explained by each of the variables, we compared the total variance between each of the models.
The R-square value indicates how much of the variation in the data is explained by the model.
BgPCAs of morphological shape and acoustic parameters showed that most variation in the data is explained by intraspecific variation.
Indeed, the first component explained less than 10% of the variance, confirming that all the information in the data is explained by the latent measure [ 57].
A total of 63.4 % of the variance in the data was explained by the measures and with a perfect model fit, this was expected to be 63.1%%.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com