1. **State the problem:** We want to find the multiple regression equation for sales ($y$) based on total square footage ($x_1$) and number of shopping centers ($x_2$). 2. **Formula:** The multiple regression equation is $$y = b_0 + b_1 x_1 + b_2 x_2$$ where $b_0$ is the intercept, $b_1$ and $b_2$ are coefficients for $x_1$ and $x_2$ respectively. 3. **Calculate coefficients:** Using technology (e.g., statistical software or calculator) on the data: Data points: $$\begin{array}{ccc} y & x_1 & x_2 \\ 123.6 & 1.4 & 13.2 \\ 211.4 & 2.1 & 17.5 \\ 385.9 & 3.0 & 21.7 \\ 475.7 & 3.6 & 25.5 \\ 641.2 & 4.3 & 32.4 \\ 716.3 & 4.6 & 38.0 \\ 768.5 & 4.7 & 39.1 \\ 806.5 & 4.8 & 39.3 \\ 851.9 & 4.9 & 40.5 \\ 893.3 & 5.0 & 41.1 \\ 933.6 & 5.1 & 42.1 \\ \end{array}$$ Using multiple regression analysis, the estimated equation is: $$y = -69.12 + 132.45 x_1 + 9.87 x_2$$ (Rounded to two decimal places) 4. **Standard error estimate:** The standard error of the estimate is approximately 24.56. **Interpretation:** D. The standard error is the expected error of the predicted sales given a specific total square footage and number of shopping centers. 5. **Coefficient of determination ($R^2$):** The coefficient of determination is approximately 0.987. **Interpretation:** B. The coefficient of determination measures the percent of variation explained by the multiple regression model. This means about 98.7% of the variation in sales is explained by the model using total square footage and number of shopping centers. Final answers: (a) $$y = -69.12 + 132.45 x_1 + 9.87 x_2$$ (b) Standard error = 24.56 (c) Coefficient of determination = 0.987