The sixth part forecasts the future logistics demand of Shandong Province on the basis of one linear regression, polynomial regression, gray system G(1,1) model and combination forecast model.
第六部分采用一元线性回归、多项式回归、灰色系统G（1，1）模型和组合预测模型对山东省现代物流业未来需求状况进行了中期预测。
The traditional logistics transportation amounts prediction ways mainly indicate liner regression and time sequence model.
传统的物流运输量预测技术大多采用线性回归和时间序列技术。
The traditional logistics transportation amounts prediction ways mainly indicate liner regression and time sequence model.
传统的物流运输量预测技术大多采用线性回归和时间序列技术。
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