A financial analyst is examining the results of a multiple regression analysis intended to predict stock returns based on various independent variables such as interest rates, inflation, and GDP growth. The model estimates were derived using a dataset spanning 20 years, with 500 observations. During the analysis, the analyst notes that the residuals from the fitted model appear to display a clear pattern when plotted against the fitted values, suggesting non-randomness.
In multiple regression analysis, certain assumptions must be satisfied to ensure that the model produces valid results. These assumptions include linearity, independence, homoscedasticity, and normality of the residuals. A violation of these assumptions could lead to biased or inefficient estimates of the regression coefficients.
Based on this context, which of the following statements correctly addresses one of the critical assumptions of the multiple regression model regarding the residuals?