| 1. | Research in groundwater dynamic law by non - linear regression method
用非线性回归方法研究地下水动态规律 |
| 2. | Weighted linear regression method
加权直线回归 |
| 3. | Determination of copper and zinc by application of the linear regression method of multiwavelength data
应用多波长线性回归法同时测定铜锌含量 |
| 4. | Based on these factors , an allowance model on ah subsidy standard of xi ' an will be established by multiple linear regression method
并根据这些因素，利用多元线性回归的数学方法，拟合出西安市廉租住房租金配租标准的定价模型。 |
| 5. | Multiple linear regression method is applied to analysis systematic error , which leads to a satisfying model and an approach to improve the output precision
通过对实验结果误差数据的采集、系统误差判别以及多元线性回归分析，揭示了误差的影响因素，得到了一些有益的结论。 |
| 6. | The results indicate that the svr model can bring higher learning precision and excellent prediction generalization ability compared with traditional linear regression methods
与常规线性回归模型预测结果相对比，所提出的方法更易于使用，很少受不确定因素的影响，并具有较强的信息整合能力以及更高的预测准确性和可信度。 |
| 7. | Founded on the theories of statistical learning , mathematical programming and functional analysis , svr is shown to outperform the traditional multiple linear regression method from the perspective of regression accuracy
根基于统计学习、数学规划及?函分析理论，支援向量?归方法较传统的多元?归方法在?归正确性上有较好的成效。 |
| 8. | Founded on the theories of statistical learning , mathematical programming and functional analysis , svr is shown to outperform the traditional multiple linear regression method from the perspective of regression accuracy
根基于统计学习、数学规划及泛函分析理论，支持矢量回归方法较常规的多元回归方法在回归正确性上有较好的成效。 |
| 9. | Based on the correlation between groundwater quality and its influence factors , a model for dynamic prediction of groundwater quality is established by using the theory of regression analysis based on multi - element linear regression method
根据地下水水质与其影响因素之间存在的相关关系，运用回归分析理论和方法，建立了一个基于多元线性回归分析法的地下水水质动态预测模型，并将该模型用于遵义市海龙坝地下水水质的动态预测。 |
| 10. | Water quantity prediction is the base and premise of water price calculating . this paper uses moving tendency forecasting modeling , gm forecasting modeling and bp neural forecasting modeling to forecast the water requirement of the future , evaluates the forecasting results , and confirms the forecasting results ; the industry water price elasticity index and the resident water price elasticity are calculated with the multi - linear regression method ; the water resources value is evaluated with the marginal opportunity cost method considering the transferring water , other parameters are evaluated and estimated by using some methods of connecting with objective laws and estimation
用水量预测是水价制定的前提和基础，本文在进行水量预测时，采用移动平均法、灰色预测法和bp神经网络进行预测，并对预测结果进行了综合评价，确定出合理的预测结果；采用多元线性回归方法确定工业用水价格弹性和居民生活用水价格弹性指数；采用跨流域调水情况下的边际机会成本方法确定当地的水资源价值；采用主观判断和客观规律相结合的方法对其它一些参数进行了确定。 |
