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Ordinary Least Squares Regression

2021
This chapter provides an introduction to ordinary least squares (OLS) regression analysis in R. This is a technique used to explore whether one or multiple variables (the independent variable or X) can predict or explain the variation in another variable (the dependent variable or Y).
Alese Wooditch, Nicole J. Johnson, Reka Solymosi, Juanjo Medina Ariza, Samuel Langton   +4 more
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Weighted Least Squares Fitting Using Ordinary Least Squares Algorithms

Psychometrika, 1997
A general approach for fitting a model to a data matrix by weighted least squares (WLS) is studied. This approach consists of iteratively performing (steps of) existing algorithms for ordinary least squares (OLS) fitting of the same model. The approach is based on minimizing a function that majorizes the WLS loss function.
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Review of Ordinary Least Squares and Generalized Least Squares

1984
The purpose of this chapter is to review the fundamentals of ordinary least squares and generalized least squares in the context of linear regression analysis. The presentation here is somewhat condensed given our objective of focusing on more advanced topics in econometrics.
Thomas B. Fomby, Stanley R. Johnson, R. Carter Hill   +2 more
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Ordinary and weighted least-squares estimators

Canadian Journal of Statistics, 1990
Summary: We propose a method of estimating the asymptotic relative efficiency (ARE) of the weighted least-squares estimator (WLSE) with respect to the ordinary least-squares estimator (OLSE) in a heteroscedastic linear regression model with a large number of observations but a small number of replicates at each value of the regressors. The weights used
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