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Understanding OLS Assumptions for Error Variance and Covariance

Learn about the key assumptions regarding error variance and covariance in Ordinary Least Squares (OLS) regression, including the significance of E(e)=0 and cov(e)=E(ee')=s2. Discover why these assumptions are essential to demonstrate that OLS yields the Best Linear Unbiased Estimators (BLUE).

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Understanding OLS Assumptions for Error Variance and Covariance

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