A B C D E F G Rent Beds Baths Sqft 2950 1453 2400 1476 4 2375 13 1132 5 2375 1132 6 2350 1589 7 2000 1459 8 1935 1200 9 1825 1248 10 1810 898 11 1735 2. 5 1060 12 1695 1100 13 1405 1030 14 1375 924 15 1365 974 16 1325 988 17 1275 880 18 1200 712 19 1180 1. 5 890 20 1180 960 21 1115 1020 22 1100 903 23 1060 724 24 1007 1260 25 850 1. 5 890 26 810 570 27 785 475 28 744 930 29 30 31Chad Dobson has heard about the positive outlook for rental properties. He has access to monthly rents (In $) for 27 houses, along with three characteristics of the home: number of bedrooms (Beds). number of bathrooms (Baths). and square footage (Sqit). A portion of the data is shown In the accompanying table. Rent Beds Baths 2, 950 2, 400 1, 476 744 920 Click here for the Excel Data File a-1. Estimate a linear model that uses Rent as the response variable. (Round your answers to 4 decimal places.) Predicted Rent = + Beds Baths + Sqft a-2. Estimate an exponential model that uses log of Rent as the response variable. (Round your answers to 4 decimal places.) Predicted In(Rent) = + Beds + Baths + Sqit b. Compute the predicted Rent for a 1,500-square-foot house with three bedrooms and two bathrooms for the linear and the exponential models (Ignore the significance tests). (Round the regression coefficients to exactly 4 decimal places and answers to 2 decimal places.) Linear Exponential Predicted Rent c-1. Compute the value of , defined In terms of Rent. (Do not round intermediate calculations. Round your answers to 4 decimal places) Linear Exponential c-2. Which of the above two models is more appropriate for this application? O Exponential model because of higher standard error of the estimate s, (0.2067 0.79) O Linear model because of higher standard error of the estimate s, (284.62 > 0.2067) Linear model because of higher . (0.79 > 0.74)