通过逐步回归分析方法剔除次要影响因素，并采用卡尔曼滤波方法动态预测回归残差项。
The sequential regression analysis was adopted to screen off the secondary factors, and the Kalman filtering technique was used to estimate innovation coefficients of the model dynamically.
西格玛黑带应能识别在各种可能原因的列表中，哪一个原因最合适解释在回归残差中出现的非随机分布。
The Six Sigma Black Belt should be able to identify which cause on a list of possible causes will most likely explain a non-random pattern in the regression residuals.
然后采用谱分析技术，得出各价格的具体周期，并剔除关键诱因的影响后对回归残差进行功频谱分析，进一步对回归方程进行检验。
It has verified the regression Equation by the analysis of Power-frequency Spectrum of regression on residual value after removing the influence of the key factors.
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