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The results indicate that multivariate regression on whole spectra can be far more effective than using a few selected variables.
Table 2 shows results from the multivariate regression on level I. Model (I.1) includes key transaction-related control variables, and model (I.2) adds dummy variables on the context-related factors year, industry, and country.
Overfitting: we are aware that the number of 66/81 sequences represents the lower limit for a multivariate regression on 51 dimensions.
A different approach has been proposed that first applies a principal component analysis (PCA) to find the directions in which phenotypes are the most correlated, and then uses a multivariate regression on the projected phenotypes (Mangin et al., 1998; Weller et al., 1996).
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Table 2 summarizes the multivariate regression of comorbidities on LOS.
Allometry within genotypes was tested by multivariate regression of shape on centroid size [28], [29].
We used multivariate regression of shape on centroid size [ 53] as the general approach to analyze allometry.
From the multivariate regression of shape on ln-CS, the non-allometric residual fraction is standardized by Standard6 (IMP-software).
Allometric shape variation related to size was accounted for statistically by computing a multivariate regression of shape on size (see Materials and Methods for details).
The allometric patterns of shape variation were analyzed using linear multivariate regression of PWs on log-transformed size (ln-CS) [ 48, 50- 52].
The method relies on the multivariate regression of sample mean and variance.
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