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multiple regression model meaning in English

多重回归模型

Examples

  1. In the multiple regression model vital capacity as dependent variable , height , chest circumference and sitting height were sent into the model , standardized coefficient of the mode was 0 . 903 in boys and 0 . 802 in girls ; pulmonary ventilation as dependent variable , age and body weight were entered as predictors , standardized coefficient of the mode was 0 . 736 in boys and 0 . 748 in girls
    以肺活量为应变量,身高、胸围和坐高被引入模型,模型标化相关系数男生为0 . 903 ,女生为0 . 802 ;以肺通气量为应变量,年龄、体重被引入模型,模型标化相关系数男生为0 . 736 ,女生为0 . 748 。
  2. The second powerful item is the " layout " ( the partial correlation coefficients are above 0 . 7 , except the native - modern ) ; the least influential item is the " background color " . ( 3 ) the characteristics and performance are very similar between multiple regression model and quantification model , but the neural network model quite differs the others
    ( 3 )三种模式中其中复?归分析与数量化一类模式之特质及调查结果两者较为接近(预测相似度81 % ) ,而类神经网路与两者之结果相去较远(预测相似度分别为62 %及52 % ) 。
  3. We have combined qualitative analysis and quantitative analysis to foresee the market size . firstly , we found the relative factors influencing the truck market through qualitative analysis and picked up several main factors by quantitative analysis , such as highway mileage , social fixed assets investment capital and consumption expenditure , etc . secondly , we set up four models by using those factors . the four models are a time series model , a multiple regression model , a factor regression model and an integrated model
    首先,通过定性分析找到了影响我国载货汽车保有量的相关因素,接着又进一步进行定量的分析,从而确定了公路里程数、基本建设固定资产投资额和我国社会消费支出额等为主要影响因素;然后,利用前面的分析结果构造了三个模型,即时间序列模型、多元回归模型和因子回归模型,并综合几个模型的优点建立了一个综合的预测模型,这一部分也是全文的重点部分;最后,分析比较了各模型的优劣并给出了每个模型的适用情况。
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Related Words

  1. complete regression
  2. antitonic regression
  3. inertia regression
  4. regression region
  5. regression design
  6. regression rate
  7. linear regression
  8. robust regression
  9. regression matrix
  10. spline regression
  11. multiple regression function
  12. multiple regression line
  13. multiple regression technique
  14. multiple rejection
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