你可以基于线性、广义线性、以及混合线性模型进行单变量和多变量数据的全面分析。
You can carry out very comprehensive analysis of univariate and multivariate data based on linear, general linear, and mixed linear models.
结果表明，软件可以实现数据的不同方式拟合、多变量数据回归和未知实验数据点预测。
The data simulation in different manners, regression of multiple variables, and prediction of experimental data can be achieved by using of this software.
然而，这些目标并不容易实现，特别是当我们处理从先进的化学仪器或化工厂获得的复杂多变量数据集时。
However, these targets can not be performed easily especially with the complex multivariate data sets which can be obtained from most of the advanced chemical instruments and chemical plants.
作为进行多变量数据分析的重要手段，“图模型”近年来得到了人们的广泛关注，并被应用于许多重要领域。
Firstly, we develop some new methods on the basis of graphical models, which has been widely used in data mining and multivariate analysis.
没错，这种排名不会出现缺乏数据支持的问题---虽然以不同的角度、从不同的国家来收集数据会产生很多变量。
True, there would be no shortage of data, though it would be of variable robustness and gathered on different bases in different countries.
最后，本文探讨了第一个数据挖掘模型：回归模型(特别是线性回归多变量模型)，另外还展示了如何在WEKA中使用它。
Finally, this article discussed the first data-mining model, the regression model (specifically, the linear regression multi-variable model), and showed how to use it in WEKA.
强化干预组终止实验前共随访3.4年(中位数)，用多变量模型分析此期间数据。
Data obtained during 3.4 (median) years of follow-up before cessation of intensive treatment were analyzed using several multivariable models.
摘要：数据收集的66个国家和运行一个多变量线性回归分析。
Abstract: Data was gathered for 66 countries and a linear multi-variate regression was run.
结论：多变量随机系数模型可有效地进行多变量重复观测数据的动态变化趋势分析以及随机效应分析。
CONCLUSION: multivariate random coefficients model can effectively analyze the dynamic change trend and random effects of multivariate repeated measures data in medical research.
目的：探讨多变量缺失数据的不同处理方法对结果的影响。
Objective: To explore the results of different methods for managing multivariate missing data.
本文提出了一种利用多变量最小二乘法确定最佳数据平滑因子的方法，建立了优化平滑器特性的数学模型。
The paper presents the multi-variable least square method to determine the optimal factor of data smoothing and its amplitude-frequency characteristics and compares it with the general filter.
数据处理方法包括单因素多变量方差分析、多因素线性回归分析和t检验。
The data were treated with univariate and multivariate analysis of variance, multiple linear regression analysis and t test.
本文提出了利用人工神经网络进行地质数据多变量分析的方法。
This paper proposes to use artificial neural network on multivariate analysis of geological data.
本文利用CW测试获得的数据对我国某市所选的标准传播模型进行了实例校正并介绍了单变量校正法和多变量校正法。
The article has corrected the standard propagation model which is selected in m city of our country using the data of CW test, and discussed the signal variant correction and multi-variant correction.
为了解决多层的少样本或无规则数据的建模问题，在一般多层统计模型的基础上提出了多变量整体模式的累加多层统计模型。
For modeling the multilevel few sample or irregular data, the accumulated multilevel statistical models of multivariate full model was built on ordinary multilevel statistical models.
主成分分析法是从观测数据中获取主要信息的一种多变量统计方法。
Principal Component Analysis (PCA) is a main multivariate statistical method for getting principal information from observational data.
该系统是将区域化探数据预处理后，将多个指示元素异常数据合成一种多信息数据组，经趋势面分析，由计算机输出综合多变量异常图。
After the data of regional geochemical exploration have been preprocessed. Many data of anomalies of indicator elements are combined into a group of data with multi-messages.
该系统是将区域化探数据预处理后，将多个指示元素异常数据合成一种多信息数据组，经趋势面分析，由计算机输出综合多变量异常图。
After the data of regional geochemical exploration have been preprocessed. Many data of anomalies of indicator elements are combined into a group of data with multi-messages.
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