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MFA was used in order to simultaneously analyse several tables of variables (three tables for instrumental data: volatiles, semi-volatiles and non-volatiles and one table for sensory data), thus facilitating a study of the relationship between the observations (different samples), the variables and the tables.
In the equations that follow, we use the indices i, j, m, k and l to denote the samples, the variables, the first-level clusters, the second-level clusters and the covariate level of the sample, respectively, as where N, P, M, K and L a are their respective total numbers.
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Given the remarkable heterogeneity of glycans from biological samples, the variable differences create the challenge to locate the pairs of relevant glycans from two respective samples.
Section 3 describes the sample, the variables and the methodologies used in the study.
Next, the empirical distribution is used to sample the variables ((Z_t, Y_t)) from the joint probability distribution by using (8) and (10) in Section 2.3.
All longitudinal studies described the study setting, the populations sampled, the variables and data measurement sources used, provided information on informed consent, provided descriptive characteristics of participants, and provided data on outcome events and summary measures.
Figure 1 shows samples and the variables from X1 to X5 at each sample.
The PCA output gives scores and loadings, which represent the projections of the samples and the variables, respectively, onto each PC.
The term biplot refers to the simultaneous representation of two features of a data set, usually the samples and the variables.
We also show that education—in our sample, the variable is literacy has an important protective effect on cognition.
For each DNA sample, the variable region 1 2 (V1–V2) of the bacterial 16S rRNA gene was PCR-amplified using individually barcoded primer sets.
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