你甚至不必对这些时间序列数据进行分析，因为这个工具允许你做一个散点图来比较不同数据里的搜索趋势。
You don't even have to produce an analysis on time-series data, as the tool lets you do a scatter plot to compare search trends between different data.
当基因频率矩阵中存在稀有基因时，其对应分析的散点图则呈现明显的“蹄型效应”。
When some alleles have very low frequency in the gene frequency matrix, there would be "horse-shoe effect" in the Scallergram of correspondent analysis.
结果所得的数据通过模式识别法中的主成分分析（PCA），在得分散点图中实现了对不同种类的制剂的区分。
Results Analyzing the data with principal component analysis(PCA) by model recognition, the different agents can be distinguished in the scattered plots.
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