2. 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 X1 = square feet ; Price/Square Feet; Bathrooms (Number of bathrooms). B1, B2 and B3 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 0.947575 29.38132 Response Price ($000) Summary of Fit 356.8214 28 RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 0.131114 0.097695 v Analysis of Variance 121.8935 Sum of 356.8214 Source DF Squares Mean Square F Ratio 28 Model 423883.82 141295 163.6752 Analysis of Variance Error 24 20718.29 863 Prob > F C. Total 27 444602.11 <.0001* Sum of Source DF Squares Mean Square F Ratio v Parameter Estimates Model 58293.36 58293.4 3.9234 Term Estimate Std Error t Ratio Prob>|t| 14858.0 Prob > F 0.0583 Error 26 386308.75 C. Total Intercept Square Feet 0.1895693 0.011048 Price/Sq Ft -371.4508 45.67288 -8.13 <.0001* 27 444602.11 17.16 <.0001* v Parameter Estimates 12.51 <.0001* -0.33 0.7411 1961.0355 156.728 Bathrooms -3.798639 11.36416 Term Estimate Std Error t Ratio Prob>|t| Intercept Price/Sq Ft 1089.7999 550.1965 v Effect Tests 149.87283 106.9894 1.40 0.1731 1.98 0.0583 Sum of Nparm F Ratio Prob > F Effect Tests Source DF Squares Square Feet Price/Sq Ft Bathrooms 254169.70 294.4294 <.0001* Sum of 135151.41 156.5590 <.0001* Source Nparm DF Squares F Ratio Prob > F 1 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(x1), Price/Square Feet(x2) and Bathrooms(x3) From this output, state which model (i.e., selecting a combination of variables among x1, x2, x3) seems best for predicting y and give two reasons for your choice. (Cite values in the output to support your answer.) Number RSquare Model Variables: Cp Model RMSE AICC BIC Square Feet 0.5605 86.6916 334.278 337.274 202.3524 Price/Sq Ft 1 0.1311 121.894 353.362 356.358 423.4988 Bathrooms 1 0.0534 127.230 355.761 358.758 463.5412 Square Feet,Price/Sq Ft Square Feet, Bathrooms 0.9532 28.8546 274.314 277.903 2.1117 Reason #1: 0.6494 78.9607 330.688 334.277 158.5590 Price/Sq Ft,Bathrooms 0.3817 104.860 346.573 350.163 296.4294 Square Feet,Price/Sq Ft,Bathrooms 3 0.9534 29.3813 277.172 281.105 4.0000 Reason #2: