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- single multiple regression 单多重回归
- In this lecture we introduce the multiple regression. 在本次课中，我们介绍了多元回归。
- Only age, BMI and leptin correlated to BMD by multiple regression analysis. 多元线性回归分析,仅年龄、体质量指数、瘦素与骨密度相关。
- Data were analyzed using multiple regression and canonical correlation. 所收集资料以统计方法分析进行资料处理。
- Based on the multipl regression theory the regression tquations were established for the production of electrolytic metallic manganese. 用统计学的复回归理论,建立了电解锰生产的回归方程;
- Accordingly, the end for multiple regression as against linear regression appeared quite reasonable. 因此,同线性回归相比,这种对多元回归的需要看来是颇有道理的。
- Real application indicates that multiple regression formula can have high accuracy during short-term predication with high application value. 实际应用表明，建立的多元回归公式在短期预测时达到了较高的精度，具有较高的应用价值。
- In the paper, the propertices of the roots of adjoint polynomial for a class of graph is investigated, and prove the roots to be single multiple. 摘要研究了一类图伴随多项式的根的性质，并给出了这类图的伴随根是单重的。
- Using multiple regression analysis, age, duration and RRBT were associated with RRAT (R=0.59, R 2=0.35). 前牙区RRAT校正均值为 0 5 9,后牙区为 0 12 ,差异有高度显著性。
- In a multiple regression model, age and systolic blood pressure measured at home were positively correlated with AIx. 将增强指数作为因变量 ,年龄、身高、心率、家访时的收缩压及舒张压作为自变量进行多元回归分析显示 ,受试者年龄及收缩压与增强指数呈显著的正相关。
- Multiple regression analysis showed that CTR genotypes were associated with FN BMD in postmenopausal women(P<0.05). 多元逐步回归分析提示;CTR基因型与绝经后妇女股骨颈BMD相关(P<0.;05)。
- Multiple regression models can accommodate many explanatory variables that may be correlated. 多元回归模型能容许很多解释变量，而这些变量可以是相关的。
- The multiple regression model is the most widely used vehicle for empirical analysis. 多元回归模型是实证分析中最广泛使用的工具。
- While the MSE of multiple regression is 0.1685 and the RMSE of multiple regression is 0.4105.The accuracy is 65.3846%. 而多元迴归模型的MSE为0.;1685，RMSE为0
- Bronchial responsieness to methacholine was expressed as a continuous ariable, and analyzed by multiple regression. 并连续检测气道对乙酰甲胆碱的反应性，结果进行多元回归分析。
- On multiple regression linear analysis, the inverse correlation between osteoid surface and CAC score persisted. 根据复线性迴归分析，类骨质面积与CAC分数之间呈反比；
- It is superior to traditional Multiple Regression's calculating model and GP in model's astringency and predict accuracy. 该模型在收敛性能及预测准确率等方面优于基于传统的多元回归方法及gp方法的信用风险评估模型。
- It is superior to traditional Multiple Regression's calculating model and GP in model′ s astringency and predict accuracy. 该模型在收敛性能及预测准确率等方面优于基于传统的多元回归方法及gp方法的信用风险评估模型。
- A piece-wise multiple regression procedure has been used to explore the relationships between the LAIs and the CASI data. 使用逐步回归分析方法探索LAI与CASI数据的关系。
- The collected data were analyzed by descriptive statistic, one-way ANOVA, pearson product-moment correlation, Stepwise Multiple Regression Analysis. 所得资料分别以描述统计、单因子变异数分析、积差相关与多元?归进行分析。
