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Modified model was able to explain approximately 90% of the variation in observed data compared to 82% variation in data explained by the original model.
The Goodness-of-Fit Index (GFI) and the Adjusted Goodness-of-Fit Index (AGFI) are adjustment indicators that reflect the proportion of variance-covariance in the data explained by the model.
The amount of variation in the perceived age data explained by chronological age and the aging appearance features included in the linear models were 73% and 86% for the facial images of the Danish twin and British subjects respectively; there was, therefore, considerable variation in the data unaccounted for.
The quality of the model was determined from the proportion of the variation in the original data explained by the model (the cumulative sum of squares of the entries (R2X cum)) and cross-validated cumulative Q2 (the cumulative Q2 cum) across all PCs)) [54].
Cumulative per cent explained equals the variance within the PLFA data explained by successive PCs.
By definition, the axes are ordered according to the amount of variance in the data explained by them.
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The phase-variance data explained above is processed by an algorithm developed in LabVIEW programming software.
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].
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.
Our time-resolved data are explained by intrinsic Auger and radiative recombination mechanisms as well as defect-related recombination and trapping.
All quantitative data were explained by analyst software.
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