wa 10 Q . Search in Document Home Insert Draw Design Layout References Mailings Review View Acrobat Table Design Layout "+ Share Calibri (Body) * 12 AaBbCcDdEe AaBbCcDdEe AaBbCcDc AaBbCcDdEE AaBb( AaBbCCDdEe Paste BIU abe XZ X A L A. Normal No Spacing Heading 1 Heading 2 Title Subtitle Styles Create and Share Request Pane Adobe PDF Signatures 10.38 Predicting Prices of Homes Here is some output for the HomesForSale dataset fitting a model to predict the price of a home (in $1000s) using size (in square feet), number of bedrooms, and number of bathrooms. The regression equation is + Price = 103.75 + 0.082 Size - 25.81 Beds + 84.96 Baths Predictor Coef SE Coef T P Constant 103.75 92.92 1.12 0.267 Size 0.082 0.0426 1.92 0.057 Beds -25.81 32.82 -0.79 0.433 Baths 84.96 34.48 2.46 0.015 S = 228.1 R-Sq = 19.5% R-Sq(adj) = 17.5% Analysis of Variance Source DF SS MS F P Model 3 1464285 488095 9.38 0.00001 Error 116 6033150 52010 Total 119 7497435 1. What is the predicted price for a 2500 square foot, four bedroom home with 2.5 baths? 2. Which predictor has the largest coefficient (in magnitude)? 3. Which predictor appears to be the most important in this model? 4. Which predictors are significant at the 5% level? 5 . Interpret the coefficient of Size in context. 6. Interpret what the ANOVA output says about the effectiveness of this model. 7. Interpret R2 for this model. Page 1 of 1 184 words English (United States) Focus E + 160%