The SLR Model Formulation & Assumptions

Formulate a simple linear regression model and distinguish its scientific and statistical assumptions. Interpret conditional means, errors, residuals, full rank, equal variance and error dependence, then separate Gauss–Markov conclusions from exact normal-theory inference. Prerequisites: conditional expectation, variance and the linear-algebra bridge. Scenarios emphasize exogeneity, extrapolation and careful causal language.

Further reading: Regression model assumptions, Gauss–Markov theorem.