Here is some output for fitting a model to predict the price of a home (in $1000s) using size (in square feet, SizeSqFt, different units than the variable Size in HomesForSale), number of bedrooms, and number of bathrooms. (The data are based indirectly on information in the HomesForSale dataset.)

(a) What is the predicted price for a 2500 square foot, four bedroom home with 2.5 baths?
(b) Which predictor has the largest coefficient (in magnitude)?
(c) Which predictor appears to be the most important in this model?
(d) Which predictors are significant at the 5% level?
(e) Interpret the coefficient of SizeSqFt in context.
(f) Interpret what the ANOVA output says about the effectiveness of this model.
(g) Interpret R² for this model.

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The regression equation is Price = - 217 + 0.331 SizeSqFt - 135 Beds + 200 Baths SE Coef Predictor Coef т Constant -217.0 145.9 -1.49 0.140 SizeSqFt Beds 4.55 0.000 0.33058 0.07262 -134.52 57.03 -2.36 0.020 Baths 200.03 78.94 2.53 0.013 S= 507.706 R-Sq = 46.7% R-Sq(adj) = 45.3% Analysis of Variance Source DF MS Regression 3 26203954 8734651 33.89 0.000 Residual Error 116 29900797 257765 Total 119 56104751