In the data analysis, theoretical assumptions and model was tested through Descriptive Statistical analysis, ANOVA, Bivariate Correlations analysis, and Linear Regression analysis and so on.
采用描述性统计分析、单因素方差分析、相关分析、回归分析等统计分析方法对调查数据进行分析，对理论假设和模型进行验证。
To improve the forecasting accuracy, bivariate statistics and PCA were used to prepare and analyze the data in advance.
为了提高模型的预测准确率，使用了双变量统计和主成份对数据进行预处理和分析。
In modelling the bivariate repeated measurement data, using the PROC MIXED of SAS, the correlation between data could be cut into two parts: between variables and between multiple measurements.
在双反应变量重复测量资料模型构建过程中，使用SAS的MIXED过程，将重复测量数据间的相关性分为变量之间的相关与重复测量个体值之间的相关两部分。
According the characteristics of the bivariate repeated measurement data, using the MIXED procedure of SAS software to fit linear mixed effects model.
目的：通过混合效应线性模型与单因素方差分析在重复测量资料中的应用比较，旨在说明两方法在处理重复测量资料时的应用特点。
According the characteristics of the bivariate repeated measurement data, using the MIXED procedure of SAS software to fit linear mixed effects model.
目的：通过混合效应线性模型与单因素方差分析在重复测量资料中的应用比较，旨在说明两方法在处理重复测量资料时的应用特点。
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