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Analysis Of The Comprehensive Capacity Of Xinjiang State(Land) Based On Factor Analysis

Posted on:2017-05-16Degree:MasterType:Thesis
Country:ChinaCandidate:J LiFull Text:PDF
GTID:2309330485491097Subject:Probability theory and mathematical statistics
Abstract/Summary:PDF Full Text Request
Through quantitative research methods, Xinjiang overall economy and state(ground) comprehensive capabilities for analysis. Xinjiang moderately prosperous society in 2020 to provide a reference recommendations. Details are as follows:The first Chapter Using difference autoregressive moving average models ARIMA(1,2,0) in Xinjiang from 1978 to 2014 GDP fitting and short-term forecasts. By 2015, Xinjiang’s GDP 1.018652 trillion yuan, representing an increase of 8.963 percent last year; in 2016, Xinjiang’s GDP 1.109884 trillion yuan, representing an increase of 8.12 percent last year; in 2017, Xinjiang’s GDP 12047.26, 7.87 percent over the previous year. Taking into account the greater economic development by the population, we have per capita GDP was forecast obtained in 2020 per capita GDP in Xinjiang was 66,725 yuan, basically reached the level of developed countries.The second chapter factor analysis of Xinjiang 15 states(and municipal) of 23 quantitative indicators were extracted six common factors, the contribution rate of 90.308% to yield Xinjiang state(and municipal) integrated ranked. Urumqi, Karamay, Shihezi City composite score among the top three; in Hotan, Kashi Prefecture, Kirgiz Autonomous Prefecture is ranked last. From the factor scores and the total score was found, there is a big gap between rich and poor development in Xinjiang, showing polarization, the North-South uneven development and other issues.The third chapter mainly use factor score sample cluster analysis, the first use of Kmeans clustering, clustering result is not ideal, further using the most commonly used system clustering method, the final 15 samples are divided into three categories, and on the characteristics of each class of cities analyzed.
Keywords/Search Tags:ARIMA, factor analysis, K-means clustering, hierarchical clustering
PDF Full Text Request
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