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Least squares
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Approximation method in statistics
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Linear least squares
Least squares approximation of linear functions to data
Subject of 6
Probabilistic partial least squares model: Identifiability, estimation and application
article by Said el Bouhaddani et al published September 2018 in Journal of Multivariate Analysis
A differential least-squares deconvolution method for high precision spectroscopy of stars and exoplanets – I. Application to obliquity measurements of HARPS observations of HD189733b
article by John B. P. Strachan & Guillem Anglada-Escudé published 12 August 2017 in Monthly Notices of the Royal Astronomical Society
A Partial Least Squares‐Based Consensus Regression Method for the Analysis of Near‐Infrared Complex Spectral Data of Plant Samples
article
Least-Squares Means: The R Package lsmeans
scholarly article from 'Journal of Statistical Software' published in 2016
The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses
article by S. Wold et al published September 1984 in SIAM Journal on Scientific and Statistical Computing
Computing Truncated Singular Value Decomposition Least Squares Solutions by Rank Revealing QR-Factorizations
article by Tony F. Chan & Per Christian Hansen published May 1990 in SIAM Journal on Scientific and Statistical Computing
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Wikipedia

The least squares method is a statistical technique used in regression analysis to find the best trend line for a data set on a graph. It essentially finds the best-fit line that represents the overall direction of the data. Each data point represents the relation between an independent variable.

Instance of
Scientific method
Subclass of
Regression analysis
Part of
Statistics
Discoverer / Inventor
Adrien-Marie Legendre
Carl Friedrich Gauss
References
Wikidata