An in-depth look at the Best Linear Unbiased Estimator (BLUE), its historical context, importance, and mathematical formulation in linear regression.
A theorem that under certain conditions, the ordinary least squares (OLS) estimator provides the Best Linear Unbiased Estimator (BLUE) of the linear regression coefficients. The conditions include a correct linear regression function and homoscedastic, serially uncorrelated errors for non-stochastic explanatory variables.
A comprehensive coverage of the concept of homoscedasticity, its significance in linear regression, implications of its violation, and related terms and considerations.
