For the same houses from Question 1, a multiple regression model is now used to predict the price y (in $1000) of the n = 28 Seatle home prices based on two more explanatory variables in addition to square feet. The explanatory variables are then I1 = square feet; Price/Square Feet ; Bathrooms (Number of bathrooms). I2 = Bi, B2 and 33 are the corresponding parameters in the model. For all the testing problem hereby, set significance level a = 0.05. Response Price ($000) Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 0.9534 - Response Price (So00) 0.947575 29.38132 356.8214 Summary of Fit 28 RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 0.131114 - Analysis of Variance 0.097695 121.8935 Sum of Squares Mean Square 3 423883.82 356.8214 Source DF F Ratio 28 Model 141295 163.6752 863 Prob > F Analysis of Variance Error 24 20718.29 C. Total - Parameter Estimates 27 444602.11 <.0001 Sum of Source DF F Ratio Squares Mean Square 58293.36 Model 58293.4 3.9234 Estimate Std Error tRatio Prob>lt| -8.13 <.0001 17.16 <.0001 12,51 <.0001 Term Error 26 386308.75 14858.0 Prob >F Intercept Square Feet 0.1895693 0.011048 Price/Sq Ft 1961.0355 156.728 Bathrooms -371.4508 45.67288 C. Total 27 444602.11 0.0583 Parameter Estimates -3.798639 11.36416 -0.33 0.7411 Term Estimate Std Error t Ratio Prob>t| 1.40 0.1731 1.98 0.0583 v Effect Tests Intercept Price/Sq Ft 1089.7999 550.1965 149.87283 106.9894 Sum of F Ratio Prob >F Effect Tests Source Nparm DF Squares Square Feet Price/Sq Ft Bathrooms 254169.70 294.4294 <.0001" <.0001" Sum of 135151.41 156.5590 Source Nparm DF Squares F Ratio Prob >F 96.45 0.1117 0.7411 Price/Sq Ft 58293.360 3.9234 0.0583 (d) Below are JMP summaries of different regression models for predicting selling price y based on various combinations of the variables: Square Feet(r), Price/Square Feet(r2) and Bathrooms(r3) From this output, state which model (i.e., selecting a combination of variables among r1, T2, 13) seems best for predicting y and give two reasons for your choice. (Cite values in the output to support your answer.) Model Variables: Model Square Feet Price/Sq Ft Bathrooms Square Feet, Price/Sq Ft Square Feet,Bathrooms Price/Sq Ft,Bathrooms Square Feet, Price/Sq Ft,Bathrooms AICC 0.5605 86.6916 334.278 337.274 BIC 202.3524 423.4988 463.5412 2.1117 158.5590 Reason #1: 296.4294 4.0000 Number RSquare RMSE Cp 0.1311 121.894 353.362 356.358 0.0534 127.230 355.761 358.758 0.9532 28.8546 274.314 277.903 0.6494 78.9607 330.688 334.277 0.3817 104.860 346.573 350.163 0.9534 29.3813 277.172 281.105 Reason #2: