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"Generalized least squares (GLS) is a technique for estimating the unknown parameters in a linear regression model. The GLS is applied when the variances of the observations are unequal (heteroscedasticity), or when there is a certain degree of correlation between the observations." [Wikipedia]

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Generalised least squares: from regression coefficients to correlation coefficients?

For least squares with one predictor: $y = \beta x + \epsilon$ If $x$ and $y$ are standardised prior to fitting (i.e. $\sim N(0,1)$), then: $\beta$ is the same as the Pearson correlation coefficie …
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