【摘要】：當(dāng)今社會,全國各省市的經(jīng)濟均在快速發(fā)展,對各省市的綜合實力的研究成為重要課題。若能找到一種評估各省市綜合實力的有效方法,在排名過程中確定影響排名的主要因素,針對該因素進行大力建設(shè),將大大提升全國的綜合實力。本文主要工作如下：1.主要介紹了聚類分析的判別統(tǒng)計量,以及系統(tǒng)聚類法和動態(tài)聚類法。系統(tǒng)聚類法具有根據(jù)譜系圖確定分類數(shù)目及成員的優(yōu)點,故本文使用系統(tǒng)聚類法。為避免變量的量綱影響,本文采用加權(quán)的歐式距離。2.詳細介紹了因子分析中的因子模型并求解參數(shù),為了更好的解釋公共因子和評價省市綜合實力,進行了方差的旋轉(zhuǎn)和因子得分的求解。3.利用國家統(tǒng)計網(wǎng)收集并整理的2012年度全國31個省市的八個經(jīng)濟指標(biāo)進行了實驗。首先,應(yīng)用聚類分析方法將31個省市分成四類,目的是與最終排名作對比。然后,通過計算KMO和Bartlett球形檢驗值驗證數(shù)據(jù)適合應(yīng)用因子分析方法處理；最后,應(yīng)用因子分析方法提取公共因子并計算公共因子的得分,利用該得分評價各省市的綜合實力。實驗結(jié)果證明,位列前三的分別是廣東省、江蘇省和山東省,與聚類的分類結(jié)果大體一致,影響排名的主要因素是金融業(yè)增加值、第三產(chǎn)業(yè)增加值和住宿餐飲業(yè)增加值,與實際情況相符合。
[Abstract]:Nowadays, the economy of all provinces and cities in China is developing rapidly, so the research on the comprehensive strength of each province and city becomes an important subject. If we can find an effective way to evaluate the comprehensive strength of each province and city, determine the main factors that affect the ranking in the process of ranking, and make great efforts to construct this factor, we will greatly enhance the comprehensive strength of the whole country. The main work of this paper is as follows: 1. This paper mainly introduces the discriminant statistics of cluster analysis, as well as the systematic clustering method and dynamic clustering method. The systematic clustering method has the advantage of determining the number and members of the taxonomy according to the pedigree diagram, so the systematic clustering method is used in this paper. In order to avoid the dimensionality influence of variables, the weighted Euclidean distance. 2. The factor model in factor analysis is introduced in detail and the parameters are solved. In order to better explain common factors and evaluate the comprehensive strength of provinces and cities, the rotation of variance and the calculation of factor score are carried out. Eight economic indicators collected and collated by the National Statistical Network in 2012 from 31 provinces and cities in China were experimented with. First, 31 provinces and cities are divided into four categories by cluster analysis method, and the purpose is to compare with the final ranking. Then, the KMO and Bartlett spherical test data are calculated to verify that the data is suitable to be processed by factor analysis. Finally, the factor analysis method is used to extract common factors and calculate the scores of common factors, which is used to evaluate the comprehensive strength of each province and city. The experimental results show that Guangdong Province, Jiangsu Province and Shandong Province are the top three, and the classification results are consistent with the clustering results. The main factors affecting the ranking are the added value of the financial industry, the added value of the tertiary industry and the added value of the hotel and catering industry. In line with the actual situation.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F127;F224

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