{"title":"The line of best fit","authors":"M. Edge","doi":"10.1093/oso/9780198827627.003.0004","DOIUrl":null,"url":null,"abstract":"One way to visualize a set of data on two variables is to plot them on a pair of axes. A line that “best fits” the data can then be drawn as a summary. This chapter considers how to define a line of “best” fit—there is no sole best choice. The most commonly chosen line to summarize the data is the “least-squares” line—the line that minimizes the sum of the squared vertical distances between the points and the line. One reason for the least-squares line’s popularity is convenience, but, as will be seen later, it is also related to some key ideas in statistical estimation. The derivations of expressions for the intercept and slope of the least-squares line are discussed.","PeriodicalId":192186,"journal":{"name":"Statistical Thinking from Scratch","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Thinking from Scratch","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/oso/9780198827627.003.0004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
One way to visualize a set of data on two variables is to plot them on a pair of axes. A line that “best fits” the data can then be drawn as a summary. This chapter considers how to define a line of “best” fit—there is no sole best choice. The most commonly chosen line to summarize the data is the “least-squares” line—the line that minimizes the sum of the squared vertical distances between the points and the line. One reason for the least-squares line’s popularity is convenience, but, as will be seen later, it is also related to some key ideas in statistical estimation. The derivations of expressions for the intercept and slope of the least-squares line are discussed.