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- W4233928187 abstract "Abstract Developed about 200 years ago, the method – more formally, the criterion – of Least Squares (LS) seems likely to remain at the heart of the statistician's toolbox for the twenty first century. Virtually every text on elementary statistical methods gives some exposition and it lies at the heart of very many algorithms routinely used in modern data analysis, most notably in regression. To avoid confusion, only the criterion and the method more formally known as Ordinary Least Squares (OLS) are discussed in this article. As OLS is so strongly associated with the statistical modeling procedure of linear regression, it is appropriate to include an introduction to regression under the heading of OLS. In addition, the solution of the numerical equations associated with this model has almost become synonymous with OLS, and therefore the solution itself is also discussed in this article, as are some extensions." @default.
- W4233928187 created "2022-05-12" @default.
- W4233928187 creator A5004153048 @default.
- W4233928187 date "2006-09-15" @default.
- W4233928187 modified "2023-10-04" @default.
- W4233928187 title "Least Squares" @default.
- W4233928187 cites W2498377200 @default.
- W4233928187 doi "https://doi.org/10.1002/9780470057339.val009" @default.
- W4233928187 hasPublicationYear "2006" @default.
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