{"title":"比较R平方","authors":"Min S. Kim","doi":"10.2139/ssrn.3790200","DOIUrl":null,"url":null,"abstract":"This paper proposes a simple statistic for testing statistical significance of R squared. This statistic, called \"comparative R squared,\" also tests statistical significance of the difference of R squared between null and alternative models with additional explanatory variables. The asymptotic distribution of the test statistic is a chi square distribution with the degree of freedom equal to the number of (additional) explanatory variables. Unlike the F test, this statistic is useful when dealing with non-normality. Simulations show that the test statistic behaves well and has no distortion of size, even in small samples.","PeriodicalId":260073,"journal":{"name":"Mathematics eJournal","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Comparative R Squared\",\"authors\":\"Min S. Kim\",\"doi\":\"10.2139/ssrn.3790200\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a simple statistic for testing statistical significance of R squared. This statistic, called \\\"comparative R squared,\\\" also tests statistical significance of the difference of R squared between null and alternative models with additional explanatory variables. The asymptotic distribution of the test statistic is a chi square distribution with the degree of freedom equal to the number of (additional) explanatory variables. Unlike the F test, this statistic is useful when dealing with non-normality. Simulations show that the test statistic behaves well and has no distortion of size, even in small samples.\",\"PeriodicalId\":260073,\"journal\":{\"name\":\"Mathematics eJournal\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mathematics eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3790200\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematics eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3790200","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper proposes a simple statistic for testing statistical significance of R squared. This statistic, called "comparative R squared," also tests statistical significance of the difference of R squared between null and alternative models with additional explanatory variables. The asymptotic distribution of the test statistic is a chi square distribution with the degree of freedom equal to the number of (additional) explanatory variables. Unlike the F test, this statistic is useful when dealing with non-normality. Simulations show that the test statistic behaves well and has no distortion of size, even in small samples.