According to the Gauss-Markov theorem, $\boldsymbol{\hat \beta}$ is the "best" linear unbiased estimator ([[BLUE]]) of $\beta$. The [[least squares estimator]] has the lowest variance among all unbiased estimators of $\beta$, given that the five Gauss-Markov assumptions are met: 1. Linearity 2. Random: data are a random sample from the population 3. Non-collinearity 4. Exogeneity: the regressors aren't correlated with the error term 5. Homoscedasticity > [!Tip]- Additional Resources > [Statistics How To - Gauss Markov Theorem & Assumptions](https://www.statisticshowto.com/gauss-markov-theorem-assumptions/)