{"title":"测试相关业务数据的平均值。","authors":"Jiajuan Liang, Linda Martin","doi":"10.1080/15390940802232440","DOIUrl":null,"url":null,"abstract":"<p><p>In business data analysis, it is well known that the comparison of several means is usually carried out by the F-test in analysis of variance under the assumption of independently collected data from all populations. This assumption, however, is likely to be violated in survey data collected from various questionnaires or time-series data. As a result, it is not justifiable or problematic to apply the traditional F-test to comparison of dependent means directly. In this article, we develop a generalized F-test for comparing population means with dependent data. Simulation studies show that the proposed test has a simple approximate null distribution and feasible finite-sample properties. Applications of the proposed test in analysis of survey data and time-series data are illustrated by two real datasets.</p>","PeriodicalId":84996,"journal":{"name":"Journal of hospital marketing & public relations","volume":"18 2","pages":"149-65"},"PeriodicalIF":0.0000,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/15390940802232440","citationCount":"0","resultStr":"{\"title\":\"Testing the mean for dependent business data.\",\"authors\":\"Jiajuan Liang, Linda Martin\",\"doi\":\"10.1080/15390940802232440\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In business data analysis, it is well known that the comparison of several means is usually carried out by the F-test in analysis of variance under the assumption of independently collected data from all populations. This assumption, however, is likely to be violated in survey data collected from various questionnaires or time-series data. As a result, it is not justifiable or problematic to apply the traditional F-test to comparison of dependent means directly. In this article, we develop a generalized F-test for comparing population means with dependent data. Simulation studies show that the proposed test has a simple approximate null distribution and feasible finite-sample properties. Applications of the proposed test in analysis of survey data and time-series data are illustrated by two real datasets.</p>\",\"PeriodicalId\":84996,\"journal\":{\"name\":\"Journal of hospital marketing & public relations\",\"volume\":\"18 2\",\"pages\":\"149-65\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/15390940802232440\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of hospital marketing & public relations\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/15390940802232440\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of hospital marketing & public relations","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/15390940802232440","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In business data analysis, it is well known that the comparison of several means is usually carried out by the F-test in analysis of variance under the assumption of independently collected data from all populations. This assumption, however, is likely to be violated in survey data collected from various questionnaires or time-series data. As a result, it is not justifiable or problematic to apply the traditional F-test to comparison of dependent means directly. In this article, we develop a generalized F-test for comparing population means with dependent data. Simulation studies show that the proposed test has a simple approximate null distribution and feasible finite-sample properties. Applications of the proposed test in analysis of survey data and time-series data are illustrated by two real datasets.