least squares

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least squares

(lēst skwārz),
A principle of estimation invented by Gauss in which the estimates of a set of parameters in a statistical model are the quantities that minimize the sum of squared differences between the observed values of the dependent variable and the values predicted by the model.

least squares

a method of regression analysis. The line on a graph that best summarizes the relationship between two variables is the one that ensures that there is the least value of the sum of the squares of the deviation between the fitted curve and each of the original data points.
References in periodicals archive ?
Ordinary least squares regressions using clustered standard errors to control for repeated observations from the same state were used to estimate the coefficients of the Reform variable.
Shimizu and Nishimura (2007) estimated using ordinary least squares hedonic price equations of commercial and residential land prices in Tokyo for a 25-year period (from 1975 to 1999) and investigated possible structural changes in these price equations.
Robustness Issues: Core Versus Headline Inflation and Ordinary Least Squares Versus Instrumental Variables
On the other hand, ordinary least squares (OLS) models the relationship between a dependent variable and a collection of independent variables.
Undergraduate econometric textbooks generally state the Gauss-Markov theorem as follows: Among all linear unbiased estimators, the ordinary least squares (OLS) estimator is best in the sense that it has minimum variance [Gujarati 1995; Pindyck and Rubinfeld 1997; Studenmund 1997].
We conduct tests by using both ordinary least squares and two-stage least squares regression models and various measures of board independence.
The regressions follow the dynamic ordinary least squares method as described in the article.
These factors, together with the very limited number of observations (22), lead us to rely upon the small-sample strengths of ordinary least squares to estimate equation (3) rather than upon the asymptotic properties of grouped-data logit (or probit) or weighted least squares.
Ordinary least squares (OLS) is the usual method for carrying out regression analysis, and it is optimal in the sense that it summarizes the information in the data.
The ordinary least squares estimate for a particular explanatory variable indicates the change in the monthly apartment rent per unit of change in the explanatory variable, holding constant the remaining variables in the regression.
The results using the SUR technique are not substantially different from those obtained using the more traditional Ordinary Least Squares (OLS) approach.