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Mark K. Ehlenfeldt, James J. Polashock, Allan W. Stretch, and Matthew Kramer

2 years that share at least two cultivars (because standards were present in most years for both mummy blight and fruit rot data sets, typically many cultivars were shared for randomly selected pairs of years). We use a principal components analysis

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Amy F. Iezzoni and Marvin P. Pritts

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S. Pérez, S. Montes, and C. Mejía

weight ratio; GH, growth habit; GP, germination period; HS, harvest season; La, leaf area; NW, nodes with zero buds: Nl b, nodes with one bud; N2b. nodes with two buds; N3b. nodes with three buds; P, productivity; PCA, principal component analysis: Prec

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Esther Giraldo, Margarita López-Corrales, and José Ignacio Hormaza

selected variable categories was also reduced by the Pearson correlation coefficient and by principal components analysis (PCA) ( Iezzoni and Pritts, 1991 ). These analyses were performed on the correlation matrix obtained with the frequencies, turning the

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Yun-Peng Zhong, Zhi Li, Dan-Feng Bai, Xiu-Juan Qi, Jin-Yong Chen, Cui-Guo Wei, Miao-Miao Lin, and Jin-Bao Fang

between a particular growth variable and drought tolerance was expressed by Eq. [ 1 ], whereas a negative correlation was expressed by Eq. [ 2 ]. Principal component analysis was used to extract the common factors associated with the cumulative variance

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Jinwook Lee and Kenneth W. Mudge

ginsenoside Rg1 was positively and negatively correlated with root ginsenoside Rg1 and Re, respectively. Principal component analysis models were performed to provide the overall responses of all the ginsenosides to the given ginseng populations, depending on

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Marjorie Reyes-Díaz, Claudio Inostroza-Blancheteau, Rayen Millaleo, Edgardo Cruces, Cristián Wulff-Zottele, Miren Alberdi, and María de la Luz Mora

or/and biochemical profiles best characterized the leaf phenotypes of the cultivars with contrasting Al tolerance, we applied principal component analysis (PCA) ( Roessner et al., 2001 ; Taylor et al., 2002 ). Principal component analysis is a method

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Winston Elibox and Pathmanathan Umaharan

the association between the parameters. Where a correlation was strong, linear regression analysis was performed to determine the nature of the relationship. The quantitative data for the 13 parameters were subjected to principal component analysis

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Mohsen Hesami and Mostafa Rahmati-Joneidabad

religiosa L. According to Table 4 , PCA showed that the first two factor components explained 84.51% of the variation. Table 4. Principal component analysis of morphological traits. The first factor component had the positive relationship with leaf, tree

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Timothy K. Broschat

being severe and 5 being completely free of deficiency symptoms for that element. Because plant size variables, such as height, width, and stem caliper, are typically highly intercorrelated, principal component analysis was performed on the data to