Final answer:
To assess the quality of fit for the regression equation, we need statistical measures like R-squared and analysis of residuals. Without these, we cannot conclude if the equation provides a good fit.
Step-by-step explanation:
To determine if the estimated regression equation provides a good fit to the data, we would typically look at statistical measures such as R-squared (r²) and standard error of the estimate. However, these specific statistics are not provided in the question. Instead, the regression equation is given as Y = -5531.01 - 1386.21x₁ + 60.28x₂ + 54797.08x₃, where x₁ denotes the number of bathrooms, x₂ denotes square footage, and x₃ denotes the number of bedrooms, with y denoting the selling price. Without additional data like r² values or residual analysis, it's not possible to assess the quality of fit for this model. Regression analysis often involves checking assumptions such as linearity, homoscedasticity, and normality of residuals.