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As an additional check of sensitivity to over-fitting, we randomly re-assigned expression data to the samples, such that original and randomized data were not correlated, and assessed the ability of PLS to construct a model.
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We used descriptive data to compare the samples as well as inferential statistical procedures to compare differences in the outcomes.
Hierarchical clustering and principal component analysis was performed on the normalized signal data to assess the sample relationship and variability.
We used only fossil data to avoid the sampling and analytical complexities of comparing fossil with modern samples.
Matt Erlendson, Yale Medical Student, on the importance of patient-donated data to increase the sample size and diversity of medical research.
Due to varying number of awards given in a single year among these eight awards, it is necessary to use multiple years of data to expand the sample size.
We coded the allele frequency and then aggregated the data to the nation level weighted by sample size.
Several models were built that related remotely sensed data to the measured plot data in each sampled 400 m2 cell.
To conduct further comparisons of our data sample to the data samples used in related studies we form portfolios from single sorts on characteristics.
The mean shift algorithm [2] uses the repeated movement of data points to the sample means.
To account for the sampling error when drawing samples from the prefectural level, we weighted the data according to the sampling proportions of each region.
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