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Research on the order parameter selection algorithm based on correlation analysis and principal component analysis——Taking the Logistics sector in Gansu Province as an example

机译:基于相关分析和主要成分分析的顺序参数选​​择算法研究 - 以甘肃省物流部门为例

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An order parameter selection algorithm based on correlation analysis and principal component analysis was designed according to the statistical analysis method, the selection principle of order parameters of social system, and the correlation test in correlation analysis and the variable contribution test in principal component analysis in this paper. The redundant variables were eliminated from the system by correlation analysis first, and then the variables with high contribution to the system were selected by principal component analysis, so the order parameters obtained accordingly not only have low information redundancy, but also reflect the actual information of the social system to the greatest extent. At the end of this paper, the logistics sector in Gansu Province was taken as an example to select the panel data from 2006 to 2015. Eight indices were extracted as the order parameters of the logistics sector in Gansu Province from the sixteen indices which are redundant selected by this algorithm. The order parameters selected by rational judgment reflect 99% of the original information. The results show that the order parameters in the social system can be correctly and reasonably selected by this order parameter selection algorithm based on correlation analysis and principal component analysis.
机译:根据统计分析方法,社会系统顺序参数的选择原理设计了基于相关分析和主成分分析的订单参数选择算法,以及相关分析中的相关性试验和主成分分析中的变量贡献试验纸。通过相关性分析从系统中消除了冗余变量,然后通过主成分分析选择具有高贡献的变量,因此相应地获得的订单参数不仅具有低信息冗余,而且还反映了实际信息社会系统最大程度。本文末尾,甘肃省物流部门为例,以便从2006年到2015年选择面板数据。提取八个指数作为甘肃省物流部门的秩序参数,这是多余的十六个指标通过该算法选择。理性判断选择的订单参数反映了原始信息的99%。结果表明,通过基于相关分析和主成分分析,可以通过该阶参数选择算法正确地选择社会系统中的订单参数。

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