Assumptions of the Classical Linear Regression Model

The Classical Linear Regression Model (CLRM) relies on key assumptions to ensure that Ordinary Least Squares (OLS) estimators are unbiased, efficient, and consistent. This topic is critical for Zambian university students in econometrics, statistics, and applied research programs. It covers assumptions such as linearity, random sampling, zero conditional mean, homoskedasticity, and normality of errors. Understanding these assumptions helps students diagnose model validity, interpret results accurately, and choose appropriate estimation techniques. Mastery of CLRM assumptions is essential for conducting rigorous empirical research, contributing to evidence-based policy in Zambia, and succeeding in tertiary-level examinations and professional data analysis roles.