逐步回归方法 meaning in English
stepwise regre ion method
stepwise regression method
Examples
- Chapter 5 based on studying the factors affecting the pricing of ipos , combining multifactor step regress method , concludes ipos pricing model , demonstrate the factors affecting the pricing of ipos
第五章运用多元因素逐步回归方法，构建ipo定价模型，进一步深入研究ipo定价影响因素。 - Based on the capacitance distributions from 12 - electrod capacitance sensor , using stepwise regression method , the capacitance relation of oil - gas two phase flow based on flow pattern is developed
摘要基于12电极阵列电容传感器提供的电容测量信息，采用逐步回归方法，获得了与流型相关的电容关联式。 - Through the methods of multiple linear stepwise regressions , the main environment factors were found and multiple linear regression models among the transpiration rate and environment factors were set up
通过多元线性逐步回归方法，得到了影响沙质海岸蒸腾速率的主要环境因子，及其与土壤呼吸速率之间的多元回归模型。 - ( 6 ) the reservoir operation function is established utilizing the optimal dispatching results . the state variable and decision variable of the operation function is discussed , and the stepwise regression method is used to derive the operation function . because of the nonlinear features of the function have n ' t been reflected in traditional regression methods , the back - propagation neural network model is introduced to establish the operation function
( 6 )利用水库优化调度结果建立水库调度函数，在分析水库调度函数各特征量的基础上，介绍了用逐步回归方法建立水库调度函数的具体过程，考虑到传统回归方法未能反映调度函数的非线性特性，引入bp神经网络模型求解模型，建立水库调度函数。 - Evidence suggests that the prognostic ability of the new model with high stability , when hidden nodes changing nearby input nodes and training times changing at the certain extent , is significantly better than traditional step wise regression model mainly due to the new model condensing the more forecasting information , properly utilizing the ability of ann self - adaptive learning and nonlinear mapping . but the linear regression technique only selects several predictors by the f value , many predictors information with high relative coefficients is not included . so the new model proposed in this paper is effective and is of a very good prospect in the atmospheric sciences fields
进一步深入分析研究发现，本文提出的这种基于主成分的神经网络预报模型，预报精度明显高于传统的逐步回归方法，其主要原因是这种新的预报模型集中了众多预报因子的预报信息，并有效地利用了人工神经网络方法的自组织和自适应的非线性映射能力；而传统的逐步回归方法是一种线性方法，并且逐步回归方法只是根据f值大小从众多预报因子中选取几个预报因子，其余预报因子的预报信息被舍弃。