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.
用多变量线性回归方法计算不同母乳喂养方式和认知能力之间的粗回归系数和校正的回归系数。
Multivariable linear regressions were created to estimate crude and adjusted relations of various BF measures and later cognitive ability.
多变量线性回归模型，然后用来评估联合的贡献顶端的SNP协会和互动，以abi的调整后变。
Multivariable linear regression models were then used to assess the joint contributions of the top SNP associations and interactions to ABI after adjustment for covariates.
在深入学习更高级的技术(如多次回归或多变量方差分析)之前，对于简单线性回归的透彻理解将使您受益匪浅。
Before you plunge into learning more advanced techniques, like multiple regression or manova, you could benefit from having a solid understanding of simple linear regression.
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.
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.
实证部分主要包括多变量检验、单变量t检验、趋势图分析以及多元线性回归分析等。
Evidences include some of the major multi-variable tests, a single variable t tests, trend analysis and multiple linear regression analysis.
实证部分主要包括多变量检验、单变量t检验、趋势图分析以及多元线性回归分析等。
Evidences include some of the major multi-variable tests, a single variable t tests, trend analysis and multiple linear regression analysis.
应用推荐