{"title":"二氧化碳排放。多变量分析hj双图、聚类双图和聚类不相交双图","authors":"Pilacuan Bonete Luis, Galindo Villardon Purificación","doi":"10.11159/ICEPR19.160","DOIUrl":null,"url":null,"abstract":"This document studies graphically, through cluster groups, 17 countries in Europe and South America, generating an order with respect to different variables of public spending, education, environmental, public security, this in order to know the relationship they have with the Carbon Dioxide emissions variable, and generate a multivariate appreciation, using a comparison between the HJBiplot methods of the MulBiplot software, Clustering Biplot and Clustering Disjoint Biplot, using the RStudio software. The clusters obtained allow us to interpret in a broader context the relationship and variability of each country in relation to a set of variables, and to know the homogeneity between countries. In conclusion, using the three grouping methods with certain similarities since all three use the HJ-Biplot within their processes, but differ in others, it was possible to observe how the carbon dioxide emissions, considered as one of the gases causing the greenhouse effect maintains a positive relationship with the economic growth of the countries represented by the GDP per capita, since in the three groups by cluster both variables remain always related, while the variable of Expenditure in Research presents a positive relationship also with respect to these variables, however in the CDBiplot is part of a different factorial axis than the other two variables.","PeriodicalId":265434,"journal":{"name":"Proceedings of the 5th World Congress on New Technologies","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Carbon Dioxide Emissions. A Multivariate Analysis HJ-Biplot, Clustering Biplot and Clustering Disjoint Biplot\",\"authors\":\"Pilacuan Bonete Luis, Galindo Villardon Purificación\",\"doi\":\"10.11159/ICEPR19.160\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This document studies graphically, through cluster groups, 17 countries in Europe and South America, generating an order with respect to different variables of public spending, education, environmental, public security, this in order to know the relationship they have with the Carbon Dioxide emissions variable, and generate a multivariate appreciation, using a comparison between the HJBiplot methods of the MulBiplot software, Clustering Biplot and Clustering Disjoint Biplot, using the RStudio software. The clusters obtained allow us to interpret in a broader context the relationship and variability of each country in relation to a set of variables, and to know the homogeneity between countries. In conclusion, using the three grouping methods with certain similarities since all three use the HJ-Biplot within their processes, but differ in others, it was possible to observe how the carbon dioxide emissions, considered as one of the gases causing the greenhouse effect maintains a positive relationship with the economic growth of the countries represented by the GDP per capita, since in the three groups by cluster both variables remain always related, while the variable of Expenditure in Research presents a positive relationship also with respect to these variables, however in the CDBiplot is part of a different factorial axis than the other two variables.\",\"PeriodicalId\":265434,\"journal\":{\"name\":\"Proceedings of the 5th World Congress on New Technologies\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 5th World Congress on New Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.11159/ICEPR19.160\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 5th World Congress on New Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11159/ICEPR19.160","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Carbon Dioxide Emissions. A Multivariate Analysis HJ-Biplot, Clustering Biplot and Clustering Disjoint Biplot
This document studies graphically, through cluster groups, 17 countries in Europe and South America, generating an order with respect to different variables of public spending, education, environmental, public security, this in order to know the relationship they have with the Carbon Dioxide emissions variable, and generate a multivariate appreciation, using a comparison between the HJBiplot methods of the MulBiplot software, Clustering Biplot and Clustering Disjoint Biplot, using the RStudio software. The clusters obtained allow us to interpret in a broader context the relationship and variability of each country in relation to a set of variables, and to know the homogeneity between countries. In conclusion, using the three grouping methods with certain similarities since all three use the HJ-Biplot within their processes, but differ in others, it was possible to observe how the carbon dioxide emissions, considered as one of the gases causing the greenhouse effect maintains a positive relationship with the economic growth of the countries represented by the GDP per capita, since in the three groups by cluster both variables remain always related, while the variable of Expenditure in Research presents a positive relationship also with respect to these variables, however in the CDBiplot is part of a different factorial axis than the other two variables.