Chad Dobson has heard about the positive outlook for real estate investment in college towns. He is interested in investing in Davis, California, which houses one of the University of California campuses. 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 (Sqft). A portion of the data is shown in the accompanying table.

Exercise 7-33 Algo Chad Dobson has heard about the positive outlook for real estate investment in college towns. He is interested in investing in Davis, California, which houses one of the University of California campuses. 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 (Sqft). A portion of the data is shown in the accompanying table. Beds Rent 2,950 2,400 4 Baths 4 2 . Sqft 1,453 1,476 4 . 744 2 1 930 Click here for the Excel Data File a-1. Estimate the linear model that uses Rent as the response variable. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) Rent = 65.83 237.85 Beds + 389.37 X Baths + 0.18 Soft a-2. Estimate the exponential model that uses log of Rent as the response variable. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) In(Rent) = 6.29 X + 0.13 X Beds + 0.22 Baths + 0.00 Sqft 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). (Do not round intermediate calculations and round your final answers to 2 decimal places.) Linear 1,832.76 X Exponential 1,683.28 X Rent c-1. Find the the proportion of the sample variation in rent that is explained by the estimated regression. Note that for the exponential model, you have to calculate R2 for rent and not log of rent. (Do not round intermediate calculations. Round your answers to 4 decimal places.) Linear 0.7935 X Exponential 0.7408 X R2 c-2. Use R2 to select the appropriate model for prediction. Exponential model Linear model * Beds Baths 4 4 4 N 3 3 3 3 3 Rent 2950 2400 2375 2375 2350 2000 1935 1825 1810 1735 Sqft 1453 1476 1132 1132 1589 1459 1200 1248 4 3 3 3 2 3 2.5 2.5 2 2 2 B 898 1060 2.5 1650 3 2 1100 3 1 1030 1500 1375 1365 2 1 924 1 N 974 988 1325 2 2 1275 2 2 880 1200 1 1 712 1180 2 1.5 890 1180 2 2 960 2 1 1020 2 1 903 1 1 3 2 1115 1100 1060 1007 850 810 785 744 724 1260 890 570 2 1 1 1.5 1 1 1 475 930 2