Confidence Bounds: Key Concepts and Variance Analysis

This lecture covers key assumptions in linear regression, including normally distributed errors and X-independent errors. It also explains the constraints on errors and the importance of residuals being normally distributed. The variance of regression parameters and the proof of variance for regression coefficients are discussed, along with the T-test for intercept and slope parameters. The lecture concludes with confidence intervals for intercept and slope parameters, emphasizing the significance of these bounds in regression analysis.