{"title":"最小二乘","authors":"S. Osterlind","doi":"10.1093/OSO/9780198831600.003.0007","DOIUrl":null,"url":null,"abstract":"This chapter focuses on the next important mathematical invention: the method of least squares. First, it sets the historical context for its invention by describing the events in France and Germany leading up to the French Revolution. Next, the chapter describes how the method of least squares was invented twice, first by Adrien-Marie Legendre (as an appendix to his celestial investigations in Nouvelles méthodes pour la détermination des orbites des comètes), and then in a more sophisticated version by Carl Gauss, in Disquisitiones Arithmeticae. After that, an easy-to-understand description of method itself is given. Thus, the chapter goes from observation to probability and on to prediction, through regression, discussing ordinary least squares (OLS), intercepts, and slopes.","PeriodicalId":312432,"journal":{"name":"The Error of Truth","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"At Least Squares\",\"authors\":\"S. Osterlind\",\"doi\":\"10.1093/OSO/9780198831600.003.0007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This chapter focuses on the next important mathematical invention: the method of least squares. First, it sets the historical context for its invention by describing the events in France and Germany leading up to the French Revolution. Next, the chapter describes how the method of least squares was invented twice, first by Adrien-Marie Legendre (as an appendix to his celestial investigations in Nouvelles méthodes pour la détermination des orbites des comètes), and then in a more sophisticated version by Carl Gauss, in Disquisitiones Arithmeticae. After that, an easy-to-understand description of method itself is given. Thus, the chapter goes from observation to probability and on to prediction, through regression, discussing ordinary least squares (OLS), intercepts, and slopes.\",\"PeriodicalId\":312432,\"journal\":{\"name\":\"The Error of Truth\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Error of Truth\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/OSO/9780198831600.003.0007\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Error of Truth","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/OSO/9780198831600.003.0007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本章重点介绍下一个重要的数学发明:最小二乘法。首先,它通过描述法国和德国导致法国大革命的事件,为其发明设定了历史背景。接下来,本章描述了最小二乘方法是如何被两次发明的,第一次是由阿德里安-玛丽·勒让德发明的(作为他在《Nouvelles msamthodes pour la dsamodedes orbites des com》中的天体研究的附录),然后是卡尔·高斯在《disquisitions arithmetica》中更复杂的版本。然后,对方法本身进行了简单易懂的描述。因此,本章从观察到概率,再到预测,通过回归,讨论普通最小二乘(OLS)、截距和斜率。
This chapter focuses on the next important mathematical invention: the method of least squares. First, it sets the historical context for its invention by describing the events in France and Germany leading up to the French Revolution. Next, the chapter describes how the method of least squares was invented twice, first by Adrien-Marie Legendre (as an appendix to his celestial investigations in Nouvelles méthodes pour la détermination des orbites des comètes), and then in a more sophisticated version by Carl Gauss, in Disquisitiones Arithmeticae. After that, an easy-to-understand description of method itself is given. Thus, the chapter goes from observation to probability and on to prediction, through regression, discussing ordinary least squares (OLS), intercepts, and slopes.