Yaqian Qi, Yu Andy Li, JiaminMoran Huang, J. Huang, Heping Pan
{"title":"基于半协方差的2020年总统大选社会经济关联分析","authors":"Yaqian Qi, Yu Andy Li, JiaminMoran Huang, J. Huang, Heping Pan","doi":"10.1109/INDIN45523.2021.9557577","DOIUrl":null,"url":null,"abstract":"The expectation of an engineering observation random quantity is the first order origin moment. Variance is a special case of covariance, when two variables are the same. The variance is the second-order central moment, its root is standard deviation. The normalization of covariance to standard deviation is called Pearson correlation coefficient. The covariance for the region above or below the average is called semi-covariance (upper or down). Here we present semi-covariance, an accurate ReLU (Rectified Linear Unit) way of measuring the non-linear correlation between variables. Our framework is applied to successfully analyze the association between alternative factors and the poll response. The result of our analyses of the 2020 USA presidential election suggest that stock, pandemic, funding, culture, and mental health have different impacts on presidential candidates: Biden vs Trump. The voters care about the economy (stock and funding situations), pandemic impacted voter’s culture fairness income, and voters’ stress leading to mental issue. That’s why we picked above five factors. Whoever win more number of strong correlations is predicted as the winner.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"54 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Social Economy Association Analysis for the 2020 Presidential Election with Semi-Covariance\",\"authors\":\"Yaqian Qi, Yu Andy Li, JiaminMoran Huang, J. Huang, Heping Pan\",\"doi\":\"10.1109/INDIN45523.2021.9557577\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The expectation of an engineering observation random quantity is the first order origin moment. Variance is a special case of covariance, when two variables are the same. The variance is the second-order central moment, its root is standard deviation. The normalization of covariance to standard deviation is called Pearson correlation coefficient. The covariance for the region above or below the average is called semi-covariance (upper or down). Here we present semi-covariance, an accurate ReLU (Rectified Linear Unit) way of measuring the non-linear correlation between variables. Our framework is applied to successfully analyze the association between alternative factors and the poll response. The result of our analyses of the 2020 USA presidential election suggest that stock, pandemic, funding, culture, and mental health have different impacts on presidential candidates: Biden vs Trump. The voters care about the economy (stock and funding situations), pandemic impacted voter’s culture fairness income, and voters’ stress leading to mental issue. That’s why we picked above five factors. Whoever win more number of strong correlations is predicted as the winner.\",\"PeriodicalId\":370921,\"journal\":{\"name\":\"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)\",\"volume\":\"54 3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INDIN45523.2021.9557577\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN45523.2021.9557577","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Social Economy Association Analysis for the 2020 Presidential Election with Semi-Covariance
The expectation of an engineering observation random quantity is the first order origin moment. Variance is a special case of covariance, when two variables are the same. The variance is the second-order central moment, its root is standard deviation. The normalization of covariance to standard deviation is called Pearson correlation coefficient. The covariance for the region above or below the average is called semi-covariance (upper or down). Here we present semi-covariance, an accurate ReLU (Rectified Linear Unit) way of measuring the non-linear correlation between variables. Our framework is applied to successfully analyze the association between alternative factors and the poll response. The result of our analyses of the 2020 USA presidential election suggest that stock, pandemic, funding, culture, and mental health have different impacts on presidential candidates: Biden vs Trump. The voters care about the economy (stock and funding situations), pandemic impacted voter’s culture fairness income, and voters’ stress leading to mental issue. That’s why we picked above five factors. Whoever win more number of strong correlations is predicted as the winner.