(b) Fit an appropriate regression model that describes the fixed and variable costs for these leases. Use a dummy marginal coded as 1 for leases in the city and 0 for suburban leases. Complete the table of values for the model Term Estimate to the right. Intercept 1/Sq Ft Location Dummy Dummy x 1/Sq Ft (Round to four decimal places as needed.)(c) Does the estimated multiple regression fit in part (b) meet the conditions for the multiple regression model (MRM)? O A. No, the data for the city location do not exhibit a linear trend. O B. No, the residuals are not independent. O C. No, the data for the suburban location has much less variation than the data for the city location. O D. Yes, all of the conditions are satisfied.(d) Interpret the estimated coefficients from the equation fit in part (b), if it is OK to do so. If not, indicate why not. Choose the correct answer below. O A. The slopes indicate that the variable costs are higher for the city location. The intercepts indicate that the fixed costs are higher for the city location. O B. Both the variable and fixed costs are greater for the suburban location. O C. Both the variable and fixed costs are greater for the city location. O D. The intercepts indicate that the fixed costs are higher for the suburban location. The slopes indicate that the variable costs are higher for the city location. O E. Since the conditions for the MRM are not satisfied, the estimates cannot be interpreted.(e) Would it be appropriate to use the estimated standard errors from the output of the regression for part (b) to set confidence intervals for the estimated intercepts and slopes? O A. Yes, since confidence intervals are the only way to determine how well the model fits the data, the estimated standard errors must be used. O B. No, since the variation in the data is too different for the two locations, separate estimates are needed. O C. Yes, since the equation reproduces the data for the two locations, the estimated standard errors are accurate enough. O D. No, since the slopes and intercepts are too different, prediction intervals must be used.Does the scatterplot show a difference in the relationship for the two locations? O A. The cost for suburban locations is much more linear than the cost for city locations. B. The cost for locations in the city is on average higher than the cost for locations that are suburban. O C. The cost for city locations has negative slope, but for suburban locations, the slope is positive. O D. There is no noticeable difference.Homework: HW CH25 Question 5, 25.1.44-T HW Score: 11.98%, 0.96 of 8 points Part 2 of 7 Points: 0.33 of 1 Save The accompanying data table includes the annual prices of 25 commercial leases. All of these leases provide office space in a Midwestern city in the United States. The intercept estimates the variable costs and the slope estimates the marginal cost. Some of these leases cover space in the downtown area, whereas others are located in the suburbs. The variable Location identifies these two categories. Complete parts a through e below. Click the icon to view the data table. (@) Create a scatterplot of the cost per square foot of the lease on the reciprocal of the square feet of the lease. Do you see a difference in the relationship between cost per square foot and 1/Sq it for the two locations? Use color-coding or different symbols to distinguish the data for the two locations. Let the blue dots represent suburban locations (Location = 0) and let the red pluses represent the city locations (Location = 1). Choose the correct graph below. O A. O B. O c. 22- 22- 22- Price ($000)Sq Ft Price ($000)Sq Ft Price ($000)/Sq Ft 14- 14-4 0.5 0.5 0 0.5 1/Sq Ft (000) 1/Sq Ft (000) 1/Sq Ft (000)- X Data table Price ($000) / Sq Ft 1/ Sq Ft (000)| Location 14.2511 0.0653 17.2907 0.05240 18.3029 0.00649 15.7341 0.04374 14.8521 0.01913 15.3572 0.01970 18.3278 0.01583 15.8483 0.41563 17.3920 0.29976 15.4229 0. 1717 15.5492 0.00816 14.6190 0.03250 15.7633 0.21829 15.1289 0.015939 17.2874 0. 18765 15.6938 0.00986 15.3672 0.03096 15.3126 0.01917 17.4008 0.06055 16.9505 0.0708 16.3086 0.1758 16.5504 0.05552 17.0575 0.00657 17.9165 0. 1268 17.8913 0.06480 15.5396 0.03339 18.6629 0.03443 18.0774 0.01900 20.8122 0.20162 18.3237 0.31900