{"title":"数据变换下简单线性回归模型最小二乘参数的计算意义。","authors":"Igabari J.N","doi":"10.5987/UJ-NJSE.16.037.1","DOIUrl":null,"url":null,"abstract":"The Least squares method of parameter estimation is considered important due to its relative simplicity, optimal properties and wide economic applications. This paper examines the computational implications for the estimates of regression parameters for a simple linear regression model when there are changes in units of measurement, leading to a new set of data which is a scaled form of the original data. Expressions for the Least Squares estimates of the regression parameters are derived as well as their precision for the new data in terms of the original data.","PeriodicalId":119603,"journal":{"name":"Nigerian Journal of Science and Environment","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"COMPUTATIONAL IMPLICATIONS FOR THE LEAST SQUARES PARAMETERS OF THE SIMPLE LINEAR REGRESSION MODEL UNDER DATA TRANSFORMATION.\",\"authors\":\"Igabari J.N\",\"doi\":\"10.5987/UJ-NJSE.16.037.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Least squares method of parameter estimation is considered important due to its relative simplicity, optimal properties and wide economic applications. This paper examines the computational implications for the estimates of regression parameters for a simple linear regression model when there are changes in units of measurement, leading to a new set of data which is a scaled form of the original data. Expressions for the Least Squares estimates of the regression parameters are derived as well as their precision for the new data in terms of the original data.\",\"PeriodicalId\":119603,\"journal\":{\"name\":\"Nigerian Journal of Science and Environment\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nigerian Journal of Science and Environment\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5987/UJ-NJSE.16.037.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nigerian Journal of Science and Environment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5987/UJ-NJSE.16.037.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
COMPUTATIONAL IMPLICATIONS FOR THE LEAST SQUARES PARAMETERS OF THE SIMPLE LINEAR REGRESSION MODEL UNDER DATA TRANSFORMATION.
The Least squares method of parameter estimation is considered important due to its relative simplicity, optimal properties and wide economic applications. This paper examines the computational implications for the estimates of regression parameters for a simple linear regression model when there are changes in units of measurement, leading to a new set of data which is a scaled form of the original data. Expressions for the Least Squares estimates of the regression parameters are derived as well as their precision for the new data in terms of the original data.
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