{"title":"A study on the statistical comparison methods for engineering applications","authors":"X. Ji, S. Kang, Yanran Yu, W. Chien","doi":"10.1109/IEEM.2013.6962477","DOIUrl":null,"url":null,"abstract":"In this paper, we introduce a data comparison method, Matching Rule (MR). By setting up a relation between MR and F/T, an empirical criterion of MR for comparing two groups of data is defined. It is based on a study on the classical statistic method “Hypothesis test - F/T”. The F/T test is widely used to compare variations & means of two normal populations. However, the empirical criteria of MR do not consider type I error. To make MR truly useful, we develop a program to simulate the sample size needed for the two groups at different levels of type I error. Then MR criteria and the minimal sample size can be determined based on the required type I error. Our simulation results show that the type I error of MR can approach to the traditional F/T test method when sample size is close to 30. With a large sampling size, MR tool is more useful for engineering application than statistical comparison test [1].","PeriodicalId":6454,"journal":{"name":"2013 IEEE International Conference on Industrial Engineering and Engineering Management","volume":"19 1","pages":"576-580"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Industrial Engineering and Engineering Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEEM.2013.6962477","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, we introduce a data comparison method, Matching Rule (MR). By setting up a relation between MR and F/T, an empirical criterion of MR for comparing two groups of data is defined. It is based on a study on the classical statistic method “Hypothesis test - F/T”. The F/T test is widely used to compare variations & means of two normal populations. However, the empirical criteria of MR do not consider type I error. To make MR truly useful, we develop a program to simulate the sample size needed for the two groups at different levels of type I error. Then MR criteria and the minimal sample size can be determined based on the required type I error. Our simulation results show that the type I error of MR can approach to the traditional F/T test method when sample size is close to 30. With a large sampling size, MR tool is more useful for engineering application than statistical comparison test [1].