Here is some output for fitting a model to predict the price of a home (in s) 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.) The regression equation is:

Price = -217 + 0.331SizeSqFt - 135Beds + 200Baths

Predictor Coef SE Coef T P
Constant -217.0 145.9 -1.49 0.140
SizeSqFt 0.33058 0.07262 4.55 0.000
Beds -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 SS MS F P
Regression 3 26203954 8734651 33.89 0.000
Residual Error 116 29900797 257765
Total 119 56104751

(a) What is the predicted price for a 2100 square foot, four-bedroom home with 3.0 baths? Round your answer to the nearest hundred dollars.
(b) Which predictor has the largest coefficient (in magnitude)? Which predictor appears to be the most important in this model?
(c) Which of the variables are significant at the 5% level?