3. The regression model below examines factors that impact the DV home sale price among recently sold homes in a suburban community. The IV is total rooms within the home, and the EVs include 1) total bedrooms, 2) total bathrooms, 3) whether the home has a basement or not, and 4) the total number of days the home was on the market prior to its sale. Please answer the following regarding the regression output below: A) Identify the variables that are statistically significant predictors (at the five percent level) of a home's sale price. Explain what metrics you examined to determine each variable's statistical significance. [10] B) Explain what the coefficient for the EV "Bathrooms" means. [3] C) Explain what the coefficient for the EV "Days on Market" means. [3] D) What does the model's R-squared indicate? [4] Source SS df MS Number of obs = 96 F( 5, 90) = 9. 06 Model 7. 9698e+10 5 1. 5940e+10 Prob F 0. 0000 Res i dual 1. 5839e+11 90 1. 7599e+09 R-squared 0. 3347 Adj R-squared = 0. 2978 Total 2. 3809e+11 95 2. 5062e+09 Root MSE = 41951 saleprice coef. Std. Err. t P |t| [95% conf. Interval] totalrooms 9852. 48 4897 . 347 2. 01 0. 047 123. 045 19581. 92 bedrooms 8053. 008 10733. 63 0. 75 0. 455 -13271. 23 29377 . 24 bathrooms 43553. 03 10206. 33 4. 27 0. 000 23276. 37 63829. 69 basement 76417 . 54 42841.73 1. 78 0. 078 -8695. 044 161530.1 daysonmarket -72. 53292 63. 92549 -1.13 0. 260 -199. 5321 54. 46623 cons 108090. 3 56271. 51 1. 92 0. 058 -3702. 855 219883. 5