THE EFFECTS OF ASSUMPTION VIOLATIONS ON COEFFICIENT OF DETERMINATION AND REGRESSION MODEL ACCURACY: A STUDY OF CONSTRUCTION GDP DATA

This study emphasizes the importance of meeting key assumptions in multiple regression analysis, specifically using the GDP data from the construction sector. Reliable results depend on assumptions like the normality of errors, constant variance and independence. Our research shows that an initial model failing to meet these assumptions produced an inflated $R^2$ of 97%, misleadingly suggesting that it explained most GDP variance. After adjusting the model to fulfill the assumptions, the $R^2$ dropped to 26%. This significant decline illustrates how assumption violations distort the coefficient of determination, highlighting the need for thorough assumption validation for accurate economic analysis and informed policymaking.