21 OLS

In this video, we explore how to estimate the parent alpha and beta parameters in a regression model, specifically linking weight as the dependent variable to height as the independent variable. We delve into the concept of a fitted line, represented as weight hat equals a plus b times height, where 'a' and 'b' are our estimates for the true population parameters. The core of the discussion focuses on finding the best fitted line for our data, explaining how minimizing the sum of absolute deviations, or more commonly, the sum of squared deviations, leads to ordinary least squares. We differentiate between residuals and error terms, a crucial distinction in regression analysis. Visit AxiomTutoring.com and subscribe to @AxiomTutoringCourses.