{"title":"简单线性回归的最小二乘方法","authors":"Hasan Halit Tali̇, Ceren Çelti̇","doi":"10.54569/aair.1032607","DOIUrl":null,"url":null,"abstract":"This study approaches the least-squares method for simple linear regression model. The least-squares line does not comply with the data when there are outliers that have deceptive effects on the results in the dataset. The study aims to develop a method for obtaining a line that complies more with the data when there are outliers in the dataset.","PeriodicalId":286492,"journal":{"name":"Advances in Artificial Intelligence Research","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AN APPROACH TOWARDS THE LEAST-SQUARES METHOD FOR SIMPLE LINEAR REGRESSION\",\"authors\":\"Hasan Halit Tali̇, Ceren Çelti̇\",\"doi\":\"10.54569/aair.1032607\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study approaches the least-squares method for simple linear regression model. The least-squares line does not comply with the data when there are outliers that have deceptive effects on the results in the dataset. The study aims to develop a method for obtaining a line that complies more with the data when there are outliers in the dataset.\",\"PeriodicalId\":286492,\"journal\":{\"name\":\"Advances in Artificial Intelligence Research\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Artificial Intelligence Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54569/aair.1032607\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Artificial Intelligence Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54569/aair.1032607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
AN APPROACH TOWARDS THE LEAST-SQUARES METHOD FOR SIMPLE LINEAR REGRESSION
This study approaches the least-squares method for simple linear regression model. The least-squares line does not comply with the data when there are outliers that have deceptive effects on the results in the dataset. The study aims to develop a method for obtaining a line that complies more with the data when there are outliers in the dataset.
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