An Analysis of Influencing Factors Relating to Population Aging in China Based on SPSS

2014 ◽  
Vol 644-650 ◽  
pp. 5561-5564
Author(s):  
Zi Heng He ◽  
Yi Dan Sun
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Population Density ◽  
Influencing Factors ◽  
Linear Regression Model ◽  
Health Expenditure ◽  
Population Aging ◽  
Principal Component ◽  
Per Capita Gdp ◽  
Per Capita

In this essay, we analyze possible influencing factors which relate to population agingusing SPSS.In accord with the multivariable linear regression model, we conclude that the health expenditure of government and society along with population density have a significant correlation with population aging. Moreover, according to Principal Component Analysis (PCA), the result indicates that such factors as per capita GDP, citizen consumption and so forth have a prominent influence on population aging, and also analyze different influencing degrees of different factors during different periods.

The Quantitative Evaluation on the Response of Climate Change upon the Sustainability of the Agricultural Production

2010 ◽  
Vol 129-131 ◽  
pp. 1161-1165
Author(s):  
Lin Chun Hou ◽  
Hui Qin Li
Keyword(s):  
Climate Change ◽  
Principal Component Analysis ◽  
Linear Regression ◽  
Agricultural Production ◽  
Agricultural Productivity ◽  
Principal Component ◽  
Environmental Condition ◽  
Land Utilization ◽  
Agricultural Society ◽  
Per Capita

The aim: quantitatively evaluate the response of climate change upon the sustainability of the agricultural production. The method: the paper selected two regions (Hubei and shan’xi province) which represented different climate environment, utilized modern statistic data, Principal Component Analysis and multivariate linear regression to quantitatively evaluate the influence of climate change upon agricultural production through isolating climate environment from arable area, land utilization and management and landform and so on. The conclusion: The study indicated that when environmental condition turned good to agriculture, the function of environmental condition to agriculture relatively decreased; the capability of agricultural society and production decreased too, and people could select the land to cultivate, where agricultural productivity is higher. And that when environmental condition turned bad to agriculture, the function of environmental condition to agriculture relatively increased; the capability of agricultural society and production increased, too; people could not put emphasis on the land where agricultural productivity is higher, whereas focused on productivity per capita.

The Research of Wine Quality Evaluation Based on Multiple Linear Regression

2013 ◽  
Vol 756-759 ◽  
pp. 2489-2493
Author(s):  
Huai Hui Liu ◽  
Wen Long Ji ◽  
Peng Zhang ◽  
Chuan Wen Yao
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Multiple Linear Regression Model ◽  
Principal Component ◽  
Wine Grape ◽  
Wine Quality

Through the establishment of evaluation model based on principal component analysis, select 8 principal components from nearly 30 indexes of wine grape. Then we establish the multiple linear regression model and analyse the association between physicochemical indexes of wine grape and wine, and the influence of physicochemical indexes of wine grape and wine on wine quality. Finally study whether we could use the physicochemical indexes to evaluate the wine quality.

scholarly journals PRINCIPAL COMPONENTS TO OVERCOME MULTICOLLINEARITY PROBLEM

2019 ◽  
Vol 4 (1) ◽  
pp. 79-91 ◽  
Author(s):  
Abubakari S. Gwelo
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Combination ◽  
Principal Components ◽  
Linear Regression Model ◽  
Principal Component ◽  
Component Analysis ◽  
Ordinary Least Squares ◽  
The Impact

The impact of ignoring collinearity among predictors is well documented in a statistical literature. An attempt has been made in this study to document application of Principal components as remedial solution to this problem. Using a sample of six hundred participants, linear regression model was fitted and collinearity between predictors was detected using Variance Inflation Factor (VIF). After confirming the existence of high relationship between independent variables, the principal components was utilized to find the possible linear combination of variables that can produce large variance without much loss of information. Thus, the set of correlated variables were reduced into new minimum number of variables which are independent on each other but contained linear combination of the related variables. In order to check the presence of relationship between predictors, dependent variables were regressed on these five principal components. The results show that VIF values for each predictor ranged from 1 to 3 which indicates that multicollinearity problem was eliminated. Finally another linear regression model was fitted using Principal components as predictors. The assessment of relationship between predictors indicated that no any symptoms of multicollinearity were observed. The study revealed that principal component analysis is one of the appropriate methods of solving the collinearity among variables. Therefore this technique produces better estimation and prediction than ordinary least squares when predictors are related. The study concludes that principal component analysis is appropriate method of solving this matter.

scholarly journals Multivariate visualization of the global COVID-19 pandemic: A comparison of 161 countries

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252273
Author(s):  
Jane K. L. Teh ◽  
David A. Bradley ◽  
Jack Bee Chook ◽  
Kee Huong Lai ◽  
Woo Teck Ang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Population Density ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Time Points ◽  
Global Spread ◽  
Multivariate Visualization ◽  
Per Capita ◽  
First 90 Days

Background The aim of the study was to visualize the global spread of the COVID-19 pandemic over the first 90 days, through the principal component analysis approach of dimensionality reduction. Methods This study used data from the Global COVID-19 Index provided by PEMANDU Associates. The sample, representing 161 countries, comprised the number of confirmed cases, deaths, stringency indices, population density and GNI per capita (USD). Correlation matrices were computed to reveal the association between the variables at three time points: day-30, day-60 and day-90. Three separate principal component analyses were computed for similar time points, and several standardized plots were produced. Results Confirmed cases and deaths due to COVID-19 showed positive but weak correlation with stringency and GNI per capita. Through principal component analysis, the first two principal components captured close to 70% of the variance of the data. The first component can be viewed as the severity of the COVID-19 surge in countries, whereas the second component largely corresponded to population density, followed by GNI per capita of countries. Multivariate visualization of the two dominating principal components provided a standardized comparison of the situation in the161 countries, performed on day-30, day-60 and day-90 since the first confirmed cases in countries worldwide. Conclusion Visualization of the global spread of COVID-19 showed the unequal severity of the pandemic across continents and over time. Distinct patterns in clusters of countries, which separated many European countries from those in Africa, suggested a contrast in terms of stringency measures and wealth of a country. The African continent appeared to fare better in terms of the COVID-19 pandemic and the burden of mortality in the first 90 days. A noticeable worsening trend was observed in several countries in the same relative time frame of the disease’s first 90 days, especially in the United States of America.

scholarly journals The efficiency of provincial government health care expenditure after China’s new health care reform

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258274
Author(s):  
Xuesong Guo ◽  
Jun Zhang ◽  
Zhiwei Xu ◽  
Xin Cong ◽  
Zhenli Zhu
Keyword(s):  
Health Care ◽  
Population Density ◽  
Health Care Reform ◽  
Health Expenditure ◽  
Health Care Expenditure ◽  
Provincial Government ◽  
Per Capita Gdp ◽  
Government Health ◽  
Medical Reform ◽  
Per Capita

Objective We aim to estimate the total factor productivity and analyze factors related to the Chinese government’s health care expenditure in each of its provinces after its implementation of new health care reform in the period after 2009. Materials and methods We use the Malmquist DEA model to measure efficiency and apply the Tobit regression to explore factors that influence the efficiency of government health care expenditure. Data are taken from the China statistics yearbook (2004–2020). Results We find that the average TFP of China’s 31 provincial health care expenditure was lower than 1 in the period 2009–2019. We note that the average TFP was much higher after new health care reform was implemented, and note this in the eastern, central and western regions. But per capita GDP, population density and new health care reform implementation are found to have a statistically significant impact on the technical efficiency of the provincial government’s health care expenditure (P<0.05); meanwhile, region, education, urbanization and per capita provincial government health care expenditure are not found to have a statistically significant impact. Conclusion Although the implementation of the new medical reform has improved the efficiency of the government’s health expenditure, it is remains low in 31 provinces in China. In addition, the government should consider per capita GDP, population density and other factors when coordinating the allocation of health care input. Significance This study systematically analyzes the efficiency and influencing factors of the Chinese government’s health expenditure after it introduced new health care reforms. The results show that China’s new medical reform will help to improve the government’s health expenditure. The Chinese government can continue to adhere to the new medical reform policy, and should pay attention to demographic and economic factors when implementing the policy.

Principal component analysis for functional data

2018 ◽  
Author(s):  
Peter Hall
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Functional Data ◽  
Weighted Least Squares ◽  
Principal Component ◽  
Adaptive Methods ◽  
Component Analysis ◽  
Infinite Dimensional ◽  
Role Of Principal ◽  
Functional Linear Regression

This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how principal component functions are linked to the amount of probability mass contained in a small ball around a given, fixed function, and how this property can be used to define a simple, easily estimable density surrogate. The article concludes by explaining the use of PCA for estimating log-density.

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