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Ordinary Least Squares (OLS) is a regression method that estimates the best-fitting line by minimizing the sum of the squared residuals, where a residual is the difference between an observed value and the value predicted by the model. OLS gives every observation the same importance when fitting the regression. It works especially well when the error terms have constant variance, a condition known as homoskedasticity. If the errors have different variances across observations, called heteroskeda
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Ordinary Least Squares (OLS) is a regression method that estimates the best-fitting line by minimizing the sum of the squared residuals, where a residual is the difference between an observed value and the value predicted by the model. OLS gives every observation the same importance when fitting the regression. It works especially well when the error terms have constant variance, a condition known as homoskedasticity. If the errors have different variances across observations, called heteroskeda
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