Report on OLS Assumptions, Violations, and Remedial Strategies

This report delves into the core assumptions of Ordinary Least Squares (OLS) regression, including linearity, homoscedasticity, and the normality of errors. It identifies common violations of these assumptions and explores various remedial techniques. The report covers transformations used to address non-linear relationships and heteroscedasticity, and explains the application of generalized and weighted least squares methods. It also provides a discussion of the Gauss-Markov theorem and its relevance when assumptions are violated. The report includes screenshots and references relevant literature, offering a comprehensive overview of OLS assumptions and their practical implications for data analysis.