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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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Michel Génard and Claude Bruchou

Abbreviation: PCA, principal component analysis. 1 Station de Biométrie. We thank F. Lescourret, L. Pages, and reviewers for helpful comments on this paper, and S. Hamilton and M. Jones for improving the English translation. The PACA Region is also

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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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Eugene K. Blythe and Donald J. Merhaut

further examination and comparison of container substrates. Two such exploratory multivariate methods that require no distributional assumptions are principal components analysis (PCA) and cluster analysis (CLA). PCA, which reduces the dimensionality of

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Chia-Hsun Ho, Man-Hsia Yang, and Huey-Ling Lin

vegetable species and tends to accumulate in plant leaves ( Vila et al., 1997 ; Zoghbi et al., 1995 ). Fig. 2. A principal component analysis of the correlation between volatile compounds and aerial vegetative tissues of G. bicolor (n = 3). 25CL

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Omar Franco-Mora, Edgar Jesús Morales-Rosales, Andrés González-Huerta, and Juan Guillermo Cruz-Castillo

with the method of Pearson; then, only the highly correlated descriptors were selected and used to perform a cluster analysis by the unweighted pair group method with arithmetic mean. Finally, a principal component analysis was performed, and the

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Mostafa Farajpour, Mohsen Ebrahimi, Amin Baghizadeh, and Mostafa Aalifar

components are under more genetic control than the other components. Table 4. Principal components databased on 7 major oil compounds of 31 Iranian Achillea millefolium accessions. Conclusion Herein, a comprehensive phytochemical analysis of essential oils

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Laura P. Peña-Yam, Liliana S. Muñoz-Ramírez, Susana A. Avilés-Viñas, Adriana Canto-Flick, Jacobo Pérez-Pastrana, Adolfo Guzmán-Antonio, Nancy Santana-Buzzy, Erick A. Aguilera-Cauich, and Javier O. Mijangos-Cortés

greatest WF, whereas WF presented an inversely proportional correlation with FL (−0.573**). Table 5. Phenotypic correlation between the seven traits evaluated in 11 genotypes C. chinense Jacq. Principal components analysis. The results of the PCA ( Table

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Hrvoje Rukavina, Harrison Hughes, and Randy Johnson

trait of interest presented a function of three random factors: source locations, clones nested within locations, and blocks or replicates. Principal component analysis (PROC PRINCOMP) was performed on the data set for leaf length, canopy height, and