31 Statistical Properties Assumptions 1 and 3

In this video, we dive into the statistical properties of OLS, building on previous discussions of its mechanical properties. We'll focus on the first crucial property: OLS estimators being unbiased estimators of the true population parameters. This means that while individual sample estimates might vary, on average, they will converge to the correct population values. We begin by exploring the foundational assumptions that make this unbiasedness possible, starting with the linearity assumption, which clarifies how the parameters enter the model, not the shape of the relationship itself. We also cover the assumption requiring variation in the predictor variables, as well as the concept of no perfect collinearity in multiple regression. Subscribe to @AxiomTutoringCourses.