Statistics and Application
ISSN / EISSN : 2325-2251 / 2325-226X
Published by: Hans Publishers (10.12677)
Total articles ≅ 591
Latest articles in this journal
Statistics and Application, Volume 10, pp 317-322; https://doi.org/10.12677/sa.2021.102031
以2010~2019年中国A股医疗行业上市公司为样本，分析了研发投入、股权激励与公司绩效三者间的复杂关系。研究表明：1) 股权激励对公司绩效并无显著的反向调节作用；2) 创新投入与公司绩效有显著的正相关关系；创新投入对股权激励与公司绩效的关系无显著调节作用。关于股权激励效果出现负效应的现象，认为是高管持股比例增加所带来的控制权分散效应比较大，而高管与股东利益趋同效应对应则比较小，所以公司经济附加值随着高管持股的增加而减少。 Based on the samples of Chinese A-share medical companies from 2010 to 2019, this paper analyzes the complex relationship among R&D investment, equity incentive and corporate performance. The results show that: 1) Equity incentive has no significant effect on corporate performance; 2) There is a significant positive correlation between innovation input and corporate performance. Innovation input has no significant regulating effect on the relationship between equity incentive and corporate performance. As for the negative effect of equity incentive effect, it is believed that the decentralization effect of control brought by the increase of senior executives’ shareholding ratio is relatively large, while the convergence effect of senior executives’ and shareholders’ interests is relatively small. Therefore, the added value of corporate economy decreases with the increase of senior executives’ shareholding ratio.
Statistics and Application, Volume 10, pp 300-316; https://doi.org/10.12677/sa.2021.102030
本文选取2006~2018年中国城市群的常住人口数据，并考虑交通基础设施对人口的影响，运用Logistic模型预测了2030、2040年中国城市群人口分布情况。采用定量分析的方法，系统分析了中国城市群地区净流入人口的差异、各地区人口增减变化以及城市群人口集聚度的变化。研究结果表明：2030年至2040年期间，我国城市群人口主要呈现增长趋势，但显著增长区域由京津冀、珠三角、长三角等发展较为成熟的城市群，转向关中、中原、成渝等位于我国中部和西南地区的城市群。中国12个主要城市群人口总量将由2018年的9.92亿增长到2030年10.68亿和2040年10.92亿，相应的人口集聚度也由2018年的3.08，提高到2030年的3.19和2040年的3.53，城市群地区人口集聚态势明显，未来会有更多的人口迁往城市群地区。 This paper selected population data of China’s megaregions from 2006 to 2018, and utilized the Logistic model to predict distribution of megareions in 2030 and 2040 considering the impact of transportation infrastructure on the population. Besides, using the method of quantitative analysis, it systematically analyzed the differences in the net influx of population, the changes of population, and the agglomeration degree of population in megaregions. The results show that between 2030 and 2040, the population of megaregions will mainly show a growth trend, but the significant growth areas have shifted from the more mature megaregion such as Beijing-Tianjin-Hebei, Pearl River Delta, and Yangtze River Delta to Guanzhong, Central Plains, Chengyu, which are located in the central and southwestern regions. The total population of China’s 12 major megaregions will increase from 992 million in 2018 to 1.068 billion in 2030 and 1.092 billion in 2040. The corresponding population agglomeration index will also increase from 3.08 in 2018 to 3.19 in 2030 and 3.53 in 2040. The trend of population agglomeration is obvious, and more people will move to megaregion areas in the future.
Statistics and Application, Volume 10, pp 642-649; https://doi.org/10.12677/sa.2021.104065
目的：为分析中医药治疗高血压病的研究热点及发展趋势。方法：本文利用Citespace5.7.R2软件对作者、机构、关键词进行分析。结果：该领域的文献共纳入3517篇，近10年发文量整体趋势大致为先上升后下降。研究学者463位，研究机构338家，关键词514个。结论：研究得出我国近10年中医药治疗高血压病的研究过程，针对不同的高血压证候，采用不同的疗法以及对应的药物，注重疗效和临床效果。研究热点主要集中在老年高血压病、穴位疗法等。 Objective: To analyze the research hotspot and development trend of traditional Chinese medicine in the treatment of hypertension. Methods: The authors, institutions and keywords were analyzed by using Citespace5.7.R2 software. Results: A total of 3517 papers in this field were included, and the overall trend of the number of papers published in the last 10 years was roughly the first increase and then the decline. There are 463 researchers, 338 research institutions and 514 key words. Conclusion: The study concluded that China’s recent 10 years of traditional Chinese medicine treatment of hypertension research process, according to different hypertension syndrome, the use of different therapies and corresponding drugs, pay attention to the curative effect and clinical effect. The research focuses on the elderly hypertension, acupoint therapy and so on.
Statistics and Application, Volume 10, pp 583-592; https://doi.org/10.12677/sa.2021.104060
我国房地产近几年发展势头强盛，虽然经历了2020年的新冠疫情，但全国房价从整体上来讲还是呈现出增长的趋势。可见，国内居民对住房需求势头不减。本文利用了2012年1月到2021年1月近十年的月度房价数据，采用时间序列模型来对天津市的房价未来一段时间的价格指数进行了预测，结果显示在未来一段时间内天津市的房地产市场价格将保持一种下降的趋势，且房地产在短时间内回暖的可能性较小。 China’s real estate industry has enjoyed a strong development momentum in recent years. Despite the COVID-19 outbreak in 2020, the national housing price has shown an overall trend of growth. Visible, domestic residents for housing demand momentum is not reduced. In this paper, using the in January 2012 to January 2021, after nearly a decade of monthly house price data, by using the time series model to Tianjin price index of house prices in the future for a period of time, the results show that in the future, Tianjin real estate market prices over a period of time will maintain a downward trend, and the real estate is less likely to recover in a short time.
Statistics and Application, Volume 10, pp 593-609; https://doi.org/10.12677/sa.2021.104061
“互联网+旅游”背景下旅游网络关注度为了解景区旅游需求时空变化提供了重要的切入点。基于百度指数数据分享平台，通过获取2011~2020年北京市68家高A旅游景区的网络搜索数据，借助Python和ArcGIS软件，运用年际变动指数、季节性强度指数和地理集中指数对其进行时间和空间上的特征分析。研究发现：1) 北京市5A高等级景区和1A初等级景区数量较少，“中间大，两头小”的纺锤体结构仍有优化改进的空间。2) 旅游景区网络关注度呈现出旺季较长、平季和淡季较短的特征，并且月度特征由“双峰”向“多峰”转变，高峰出现在4月、8月和10月，与实际旅游流基本吻合。3) 节假日期间网络关注度呈现先升后降趋势，并且随着年份由倒“U”型转变为倒“V”型，其中“五一”关注度高峰出现在4月29~30日，具有明显的前兆效应，“十一”关注度高峰出现在10月2~3日，与实际旅游流趋于一致。4) 全国31个省市对北京高A景区关注度存在明显差异，表现为“近高远低、东高西低”的空间分布特征，主要是地理空间距离和社会经济发展水平综合影响的结果。 The tourism network concern degree under the background of “Internet +Tourism” provides an important breakthrough point for understanding the temporal and spatial changes of tourist demand in scenic spots. Based on the Baidu index data sharing platform, this paper obtains the online search data of 68 High-grade scenic spots in Beijing from 2011 to 2020, and uses Python and ArcGIS software to analyze their temporal and spatial characteristics by using the interannual variation index, seasonal intensity index and geographic concentration index. The results show that: 1) The number of 5A scenic spots and 1A scenic spots in Beijing is small, and the spindle structure of “big in the middle, small at both ends” still has room for optimization and improvement. 2) The network attention of scenic spots shows the characteristics of longer peak season, shorter average season and off-season, and the monthly characteristics change from “double peak” to “multi peak”. The peak appears in April, August and October, which is basically consistent with the actual tourism flow. 3) During the holidays, the network attention shows a trend of first rising and then falling, and changes from inverted “U” type to inverted “V” type with the year. The peak of attention on the “May Day” appears from April 29 to 30, which has obvious precursor effect. The peak of attention on the “National Day” appears from October 2 to 3, which is consistent with the actual tourism flow. 4) There are obvious differences in the attention of 31 provinces and cities to Beijing high-grade scenic spots, which shows the spatial distribution characteristics of “near high and far low, east high and west low”, mainly due to the comprehensive influence of geographical spatial distance and the level of social and economic development.
Statistics and Application, Volume 10, pp 132-144; https://doi.org/10.12677/sa.2021.101013
本文通过对2008~2017十年我国的外汇储备数据进行平稳性检验。采取对自相关的序列差分得到平稳序列。在平稳序列基础上建立简单ARIMA模型、简单加法季节模型和乘法季节模型并对它们进行检验。经研究，模型是有效的。最后，基于此模型对我国未来的外汇储备趋势做出简单的预测。 This paper tests the stability of China’s foreign exchange reserve data from 2008 to 2017. By difference of the autocorrelation sequence, the stationary sequence is obtained. The simple ARIMA model, the simple addition season model and the multiplication season model are established and tested on the basis of the stationary sequence. After the study, the model is effective. Finally, it makes a reasonable forecast for the future trend of China’s foreign exchange reserves.
Statistics and Application, Volume 10, pp 151-161; https://doi.org/10.12677/sa.2021.101015
目的：分析全国糖尿病疫情的时间分布特征，建立中国近年糖尿病时间序列分析的自回归移动平均模型(ARIMA)，预测病情未来发展趋势，为公众身体健康提出科学依据。方法：收集中国2000~2013年各年糖尿病患病人数数据，用R3.4.3软件构建ARIMA预测模型，对建立的模型进行参数估计、模型诊断，选择最优预测模型。利用构建的最佳模型对中国2014~2018各年糖尿病患病人数进行预测，并对预测效果进行评价。结果：ARIMA(1,1,0)模型为中国近年糖尿病人数的最优预测模型，其AIC、BIC的值分别为−38.93735、−37.80745，模型残差序列的Ljung-Box统计量，p值为0.4135，提示残差为白噪声序列，模型拟合良好。中国2014~2018糖尿病患病人数实际值与预测值的平均相对误差为2.27%，实际值均在预测值95%可信区间内。结论：ARIMA(1,1,0)模型能较好地模拟中国近年糖尿病患病人数的变化趋势，具有良好的预测效果。 Objective: To analyze the time distribution characteristics of diabetes in China, and to establish the autoregressive moving average model (ARIMA) for diabetes time series analysis in China in recent years, so as to predict the development trend of diabetes in the future and provide scientific basis for public health. Methods: The data of diabetes mellitus in China from 2000 to 2013 were collected and ARIMA prediction model was constructed by R3.4.3 software. The parameters of the model were estimated, the model was diagnosed, and the optimal prediction model was selected. The optimal model was used to predict the number of diabetic patients in China from 2014 to 2018, and the prediction effect was evaluated. Results: ARIMA (1,1,0) model was the best prediction model for the number of diabetes mellitus in China in recent years. The AIC and BIC values of ARIMA (1,1,0) were −38.93735 and −37.80745, respectively. Ljung box statistic of model residual sequence , p value was 0.4135, indicating that the residual was white noise sequence, and the model fitted well. The average relative error between the actual value and the predicted value was 2.27%, and the actual value was within the 95% confidence interval of the predicted value. Conclusion: ARIMA (1, 1, 0) model can simulate the trend of diabetes mellitus in China in recent years, and has good prediction effect.
Statistics and Application, Volume 10, pp 173-182; https://doi.org/10.12677/sa.2021.101017
通常在处理模型假设检验的问题时，统计推断是通过样本数据的观测信息来推断总体的主要方法，本文提出基于嵌套结构的分层线性回归模型的系数向量诊断方法，对于分层线性回归的第一层模型系数诊断主要利用传统的线性嵌套回归模型F检验进行统计推断。该论文的创新之处在于对分层线性回归模型的第二层系数进行统计诊断，利用嵌套多元线性回归模型推广到具有嵌套结构的分层线性回归模型中，主要构建分层线性回归模型似然函数比值来构造检验统计量。通过高校数学成绩分层数据进行分析，来验证该方法的有效性和可行性。 Generally, when dealing with the problem of model hypothesis testing, statistical inference is the main method to infer the population through the observation information of sample data. In this paper, the coefficient vector diagnosis method of Hierarchical Linear Regression Model based on nested structure is proposed. For the first level model coefficient diagnosis of hierarchical linear regression, the traditional F-test of linear nested regression model is used for statistical inference. The innovation of this paper lies in the statistical diagnosis of the second layer coefficient of Hierarchical Linear Regression Model. The nested multiple linear regression model is extended to the Hierarchical Linear Regression Model with nested structure. The likelihood function ratio of Hierarchical Linear Regression Model is mainly constructed to construct the test statistics. The effectiveness and feasibility of this method is verified by the hierarchical data analysis of college mathematics scores.
Statistics and Application, Volume 10, pp 241-255; https://doi.org/10.12677/sa.2021.102024
汇率是国际贸易中的调节杠杆，对进出口贸易收支、国内物价水平、国际资本流动和经济结构、生产布局等都会产生重要影响，因此研究汇率变动有十分重要的意义。本文主要研究澳元兑美元的汇率数值变动，选取澳元兑美元汇率变动较为显著的1979~1999年的数据进行时间序列分析，本文分别对数据拟合了ARIMA模型和GARCH模型，对模型进行各项检验，并比较了两种模型的优劣，且与目前已获得的在此之后的真实汇率值作比较以评估模型的拟合效果，最终选择GARCH模型来拟合澳元兑美元的汇率变动，并在此结果的基础上说明GARCH模型在金融数据中的广泛应用。 Exchange rate is a regulatory lever in international trade. It has an important impact on import and export trade balance, domestic price level, international capital flow and economic structure and production layout. Therefore, it is of great significance to study exchange rate fluctuations. This paper mainly studies the numerical changes of the exchange rate of Australian dollar against the US dollar, and selects the data from 1979 to 1999 when the exchange rate of Australian dollar against the US dollar changed significantly for time series analysis. In this paper, ARIMA model and GARCH model are fitted to the data respectively, various tests are carried out on the models, and the advantages and disadvantages of the two models are compared, and the real exchange rate values obtained after this are compared to evaluate the fitting effect of the models. Finally, the GARCH model is chosen to fit the exchange rate change of Australian dollar against US dollar, and the extensive application of GARCH model in financial data is illustrated on the basis of the results.
Statistics and Application, Volume 10, pp 256-268; https://doi.org/10.12677/sa.2021.102025
运用大数据推动经济转型升级、完善社会治理、提升政府服务和管理能力已成为当今社会发展的必然趋势。如何分析、挖掘、解读数据，从而让数据说真话，一直是学术界、政界和产业界关注的重要问题。众多经济问题中，居民人均收入分布一直是衡量一个地区发展的重要指标之一。本文通过对浙江省居民人均收入分布研究为例，同时通过使用MATLAB，参考多种分布模型拟合收入分布，对不同分布模型的适用程度进行研究，得到了在不同情况下不同分布的适用范围和拟合优度。同时通过对浙江省居民人均收入分布的相关数据的解读，为政府相关政策的制定提出合理的建议，为较为深入及复杂的人均收入分布问题提供了一些必要的研究基础。 It has become an inevitable trend of social development to use big data to promote economic transformation and upgrading, improve social governance and improve government service and management ability. How to analyze, excavate and interpret data so as to tell the truth is always an important issue in academia, politics and industry. In many economic problems, the distribution of per capita income has always been one of the important indicators to measure the development of a region. This article through the study of residents’ per capita income distribution in Zhejiang province as an example, through the use of MATLAB, refer to a variety of distribution model fitting of income distribution, studied the applicability of different distribution models. The applicability and goodness of fit of different distributions in different cases are obtained. According to Zhejiang province to study the distribution of the per capita income, for further on the more complex economic value per capita income distribution problem research foundation, and also puts forward suggestions on related to study the distribution of the income.