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.
采用多元统计分析、散点图和孔隙度对比图来确定其孔隙度。
In order to determine formation porosity, we use multivariate analysis.
接下来展现了响应变量与预测变量之间的散点图，分析散点图，得到了响应变量与预测变量之间的直观联系。
Next, showing and analyzing the residual plot of the response variables and predictor variables, obtain a visual link between the response variables and the predictor variables.
利用相关系数、散点图、回归分析法研究了温度与负荷的关联性。
The correlation between temperature and electricity load is studied by correlation coefficient, scatter plot and regression analysis.
运用平行图、主成分分析和非参数散点图矩阵等方法，研究多变量工业流程在时域内的变化规律。
Using a Parallel plot, PCA and Nonparametric Scatterplot Matrix to Study the Evolution in Time of a Multivariate Industrial Process.
再通过增加不同强度的噪音，分析目标在散点图上的变化规律，以便寻找目标分离的有效特征。
Different noises were added to the data, the vector variation in 2D scatter-plot can be studied. All these studies can help to make clear the effective features for targets extraction.
再通过增加不同强度的噪音，分析目标在散点图上的变化规律，以便寻找目标分离的有效特征。
Different noises were added to the data, the vector variation in 2D scatter-plot can be studied. All these studies can help to make clear the effective features for targets extraction.
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