Ordinary Least Squares
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High Quality Content by WIKIPEDIA articles! In statistics and econometrics, ordinary least squares (OLS) is a technique for estimating the unknown parameters in a linear regression model. This method minimizes the sum of squared distances between the observed responses in a set of data, and the fitted responses from the regression model. The linear least squares computational technique provides simple expressions for the estimated parameters in an OLS analysis, and hence for associated statistical values such as the standard errors of the parameters. OLS can mathematically be shown to be an op...
High Quality Content by WIKIPEDIA articles! In statistics and econometrics, ordinary least squares (OLS) is a technique for estimating the unknown parameters in a linear regression model. This method minimizes the sum of squared distances between the observed responses in a set of data, and the fitted responses from the regression model. The linear least squares computational technique provides simple expressions for the estimated parameters in an OLS analysis, and hence for associated statistical values such as the standard errors of the parameters. OLS can mathematically be shown to be an optimal estimator in certain situations, and is closely related to the generalized least squares (GLS) estimation approach that is optimal in a broader set of situations. OLS can be derived as a maximum likelihood estimator under the assumption that the data are normally distributed, however the method has good statistical properties for a much broader class of distributions.
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