基于PLS回归的单箱消耗影响因素分析—来自红河卷烟厂卷包过程中的烟丝消耗控制数据

Abstract

本文在分析了偏最小二乘回归分析和多元线性回归分析的适用条件基础上，认为偏最小二乘回归(PLS)可以有效地解决变量间多重共线性的问题，甚至适合在样本量少于变量个数的情况下进行回归建模。然后，依据红河卷烟厂烟丝消耗控制的12组样本数据，本文比较分析了偏最小二乘回归建模和多元线性回归建模的结果，发现影响因变量单箱耗丝的显著性因素为单箱废烟、单箱跑条、单箱小包机剔除量和单箱空头剔除量。因此，卷烟厂在卷包过程中的降耗工作应当首先从控制这四个单箱损耗指标开始实施，才能取得立竿见影的效果。 On the basis of some conditions for the application of partial least squares regression analysis and multivariate linear regression analysis in this paper, we can conclude that partial least squares regression (PLS) can effectively improve multicollinearity of variables. When the sample size is less than the number of variables, it also can be used to do regression modeling. Then, from 12 groups of sample data of Tobacco consumption control in Honghe Cigarette Factory, we have analyzed and compared the results of partial least squares regression modeling and multivariate linear regression modeling in the paper. It has shown that the significant factors affecting the single box consumption are single case of Wasting, single case of Running, single case of Packet rejection and single case of Short excluded volume. Therefore, the work of the cigarette factory in the process of reducing the cost should be firstly controlling these four single box loss indicators, so that we will achieve the immediate results.